Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

As a result, our architecture can produce many outputs, each with arbitrary shape and structure, and yet the latent features in our architecture remain agnostic to the shape and structure of the outputs.

The review comment to be evaluated is:

#### Strength
- The idea of perceiver IO is novel and solid -- a general architecture capable of handling general-purpose inputs and outputs across different tasks and modalities. This is very promising to simplify the construction of highly tuned task-specific neural pipelines and improve the multimodal and multi-task problems.

- Each component in perceiver IO is necessary and well defined for the proposed tasks. 2D byte array input enables the task agnostic input for different modalities. Perceiver network that scales linearly with the size of input and output, and non-auto regressive decoding make the decoding to raw output possible.

- The proposed architecture is tested on massive experiments including language understanding tasks, optical flow, video audio class autoencoding, image classification, and starcraft II and achieves superior performance. Each task is supported with a detailed ablation study to shed light on future research.

#### Weakness
- In Table 1, the WNLI task is excluded from the GLUE benchmark, I wonder what is the reason this task is removed?

- The non-autoregressive decoding enables fast decoding for large outputs. However, for tasks that require more information on prior decoding context (machine translation or image generation tasks), does the proposed model can still perform well on those tasks?

- Although perceiver IO removes the task-specific pre-processing (tokenization, patch embedding, etc.), the model still requires huge engineering efforts to adapt the model for different tasks. For example, the model hyper-parameters are quite different for different tasks.

- One huge benefit of perceiver IO is to train different tasks together and explore the transfer between different tasks/modalities, which is not explored in this paper.

The evidence to be evaluated is:Neural network research has long sought architectures that can handle large, arbitrarily structured inputs and outputs.

Similar to our work, several groups have proposed to solve tasks in multiple domains (e.g. Kaiser et al. 2017; Alyraca et al. 2020; Akbari et al. 2021), but typically across a fixed and predefined set of modalities by means of domain-specific networks.

The focus of our work is developing an architecture that is efficient and also performs well in many settings with a wide range of inputs and outputs.

Perceiver IO builds on this body of work to produce a general purpose architecture that can be easily and widely applied.

Rather than output a single category, Perceiver IO aims to have the same level of generality with respect to its _outputs_ as the Perceiver has with respect to its _inputs_: that is, it should produce arbitrary output arrays. We can predict each element of the output array using another attention module by _querying_ the latent array using a query feature vector unique to the desired output element.

We first **encode** by applying an attention module that maps input arrays \(x\in\mathbb{R}^{M\times C}\) to arrays in a latent space \(z\in\mathbb{R}^{N\times D}\). We next **process** the latents \(z\) by applying a series of modules that take in and return arrays in this latent space. Finally, we **decode** by applying an attention module that maps latent arrays to output arrays \(y\in\mathbb{R}^{O\times E}\). \(M\), \(C\), \(O\), and \(E\) are properties of the task data and can be very large (Tab. 5), while \(N\) and \(D\) are hyperparameters and can be chosen to make model computation tractable.

Perceiver IO uses attention non-homogeneously by mapping inputs to a latent space, processing in that latent space, and decoding to an output space. Perceiver IO has no quadratic dependence on the input or output size: encoder and decoder attention modules depend linearly on the input and output size (respectively), while latent attention is independent of both input and output sizes (Sec. E.2).

This architecture can be applied to inputs of any shape or spatial layout including inputs or outputs with different spatial structure (e.g. sound and video). In contrast to latent spaces typically used in vision (e.g. Ronneberger et al., 2015) the latent does not explicitly share the structure (spatial or otherwise) of the inputs.

For outputs with a spatial or sequence structure, we include a position encoding (e.g. a learned positional encoding or a Fourier feature) representing the position to be decoded in the output.

We find that even very simple query features can produce good results, suggesting that the latent attention process is able to learn to organize the relevant information in a way that's easy to query. Each output point depends only on its query and the latent array, allowing us to decode outputs in parallel.

To probe the generality of Perceiver IO, we evaluate it on several domains including language understanding (Wikipedia+C4 masked language modeling), visual understanding (Sintel/KITTI optical flow and ImageNet classification), multi-modal (Kinetics autoencoding and AudioSet classification) & multi-task settings (multi-task GLUE), and symbolic representations for games (StarCraft II).

But unlike Transformer-based models such as BERT (Devlin et al., 2019) or XLNet (Yang et al., 2019), Perceiver IO scales linearly with input length.

Perceiver IO produces one output vector per masked input by using learnable position-dependent vectors to query the output of the final latent processing layer. We then apply a position-wise linear layer on top of these output vectors and train the model using a softmax cross-entropy loss to predict the original non-masked input as target.

Our work scales to byte-level inputs without making any assumptions about the structure of the input, which allows it to be used beyond language as shown in the following sections.

Our approach is more generic as it decouples the output array from the input array by not relying on [CLS] tokens.

In contrast, we apply Perceiver IO to flow in a straightforward manner. We concatenate the frames along the channel dimension and extract a \(3\times 3\) patch around each pixel (leading to \(3\times 3\times 3\times 2=54\) values for each pixel).

With Perceiver IO, we pad the inputs with modality-specific embeddings, serialize them into a single 2D input array and query outputs using queries containing position encodings (for video and audio) and modality embeddings. We train on 16 frames at \(224\times 224\) resolution, preprocessed into 50k 4x4 patches as well as 30k raw audio samples, producing a total of 1920 16-d vectors and one 700-d one-hot class label. We decode directly into pixels, raw audio, and the one-hot label without any post-processing.

This allows us to directly decode to a video-sized array, which would otherwise be infeasible given memory constraints. We used a latent array with 512 channels and 784, 392, and 196 latents, resulting in compression ratios of 88x, 176x, and 352x respectively.

This strongly suggests that Perceiver IO can jointly represent modalities with very different properties.

In this work we introduce Perceiver IO, an architecture capable of handling general purpose inputs and outputs while scaling linearly in both input and output sizes. As we show, this architecture achieves good results in a wide variety of settings, making it a promising candidate for a _general purpose_ neural network architecture. Perceiver IO leverages the expressive power of latent attention and uses learned queries to expose a simple and unified interface that can handle multimodal and multitask settings.

These results demonstrate the unprecedented generality of Perceiver IO, the simplicity that this architecture unlocks in handling a range of tasks, and its flexibility to work as part of a domain-adapted system.

While neither the Perceiver and Perceiver IO incorporate any 2D spatial structure architecturally, they use positional features that inject 2D spatial information (Sec. 3.2 and Appendix sec. D of Jaegle et al. 2021). By replacing these 2D position features with a fully learned position encoding as used on language, we can learn an image classification model that is given no privileged information about the structure of images.

ImageNet networks that use this positional encoding are given no information about 2D image structure.

To our knowledge, this is the best result by any model on ImageNet without 2D architectural or feature information.

We use an architecture with weight sharing in depth: the latent (processing) component of the architecture includes 8 blocks of 6 attention modules each, and weights are shared between the corresponding modules in each block.

Perceiver IO is constructed from GPT-2-style (Radford et al., 2019) Transformer attention modules, which consist of QKV attention followed by an MLP, along with linear projection layers to ensure inputs to and outputs from the QKV attention and MLP take on desired sizes. Using the array sizes of the encoder attention, the QKV attention takes in two two-dimensional arrays, a key-value input array \(X_{KV}\in\mathbb{R}^{M\times C}\) and a query input array \(X_{Q}\in\mathbb{R}^{N\times D}\), and maps them to an array \(X_{QKV}\in\mathbb{R}^{N\times D}\), sharing the shape of the query input (after projection). \(X_{QKV}\) is used as input to an MLP, which is applied independently to each element of the index dimension (i.e., convolving the MLP with its input along the first dimension), producing a final array \(X_{MLP}\in\mathbb{R}^{N\times D}\).

The view of attention that we describe encompasses both cross-attention and self-attention, both of which are specific ways of using QKV-attention. Perceiver IO uses cross-attention for encoder and decoder attention modules and uses self-attention for the latent processing modules. These modules differ primarily in terms of what shape data they ingest and produce (Fig. 5).

QKV attention takes in two two-dimensional arrays, a query input \(X_{Q}\in\mathbb{R}^{N\times D}\) and a key-value input \(X_{KV}\in\mathbb{R}^{M\times C}\). The output of QKV attention is an array with the same index (first) dimension as the query input and a channel (second) dimension determined by an output projection: \[Q=f_{Q}(X_{Q}); K=f_{K}(X_{KV}); V=f_{V}(X_{KV}) \tag{1}\] \[X_{QK}=\text{softmax}(QK^{T}/\sqrt{F})\] (2) \[\text{Attn}(X_{Q},X_{KV})=X_{QKV}=f_{O}(X_{QK}V), \tag{3}\] where \(X_{QK}\) is an array of attention maps \(\in\mathbb{R}^{N\times M}\), and \(X_{QKV}\) is an array \(\in\mathbb{R}^{N\times D}\). The functions \(f_{\{Q,K,V\}}\) are linear layers mapping each input to a shared feature dimension \(F\) and \(f_{O}\) is a linear layer projecting the output to a target channel dimension, which is often the same size as \(X_{Q}\)'s. All linear layers are applied convolutionally over the index dimension (the first dimension of their inputs).

In other words, Perceiver IO has complexity linear in the size of the input and output arrays and it decouples the depth of the latent transformer from the input and output sizes. Both of these properties contribute to Perceiver IO's efficiency: while many proposals for efficient attention modules or architectures include linear or sub-quadratic scaling with input/output size, Perceiver IO is unusual in also decoupling depth from input/output size (without requiring domain-specific strategies like 2D convolution).

In addition to its greater expressivity, decoder attention has the advantage of being easily generalizable to dense outputs (by increasing the number of queries) and of reusing the same architectural pattern used for the encoder and processor modules.

The decoder outputs \(16\times 224\times 224+16\times 1920/16+1\) vectors with 512 channels, that is, one element for each pixel in the video, one element for each audio patch, and one element for the classification label. These are then linearly projected to the appropriate channel size for each modality: 3 for videos, 16 for audio and 700 for classification (the logits for each of the 700 classes in Kinetics700).

We use a 387 dimensional 3D Fourier position embedding for each input video patch and a 385 dimensional 1D Fourier position embedding for each audio patch (385 to ensure the input dimensions to Perceiver IO match for all elements). In addition, we pad all input elements with a learned vector representing the modality; inputs from the same modality share the same token.

The decoder queries are also constructed from Fourier position embeddings for video and audio and a learned positional embedding for label: 387 features for video, 385 features for audio, and 1024 learned features for the label. We pad the queries for each modality with a different learned vector for each modality, so that the final feature size for the queries is 1026.

Perceiver IO is able to capture the structure of both video and audio inputs, despite encoding both jointly with a single network.

Input: Figure 1: The Perceiver IO architecture can be used on domains with a wide variety of input and output spaces, including multi-task language understanding, dense visual tasks like optical flow, hybrid dense/sparse multimodal tasks such as video+audio+class autoencoding, and tasks with symbolic outputs like StarCraft II. See Tables 5 and 6 for details of all domains considered here.

Input: Figure 5: Schematic depiction of encode, process, and decode attention. Each attention module uses the same operations, but differs in which inputs are used to generate key/values or queries and in the output shape. Encode attention can be viewed as mapping an input to a latent space, typically with a smaller index dimension (fewer elements). Decode attention can be viewed as mapping a latent to an output space, often with a larger index dimension (more elements). Both of these are forms of cross-attention. Process attention (self-attention) preserves the input index dimension (same elements). Red and blue dashed lines are used to highlight the two matrix multiplications used in QKV attention, as described in the text.

Input: Figure 8: Qualitative examples of optical flow. For each image pair, we show the two frames (top), and then the estimated flow (bottom left) and the ground-truth flow (bottom right). In the left example, we see one person under heavy occlusion where the correct flow is propagated into a region with few details. Another person in the foreground has clothes with little texture and substantial blur, and yet the algorithm can propagate the flow across the entire region. In the center example, we see very large motions from both the dragon and the person, yet many fine structures are preserved like the pole. On the right, we see a forest scene with a few extremely small objects with very subtle motions (circled) which our algorithm is able to detect and segment correctly.

Input: Figure 2: The Perceiver IO architecture. Perceiver IO maps arbitrary input arrays to arbitrary output arrays in a domain agnostic process. The bulk of the computation happens in a latent space whose size is typically smaller than the inputs and outputs, which makes the process computationally tractable even for very large inputs & outputs. See Fig. 5 for a more detailed look at encode, process, and decode attention.

Input: Table 5: Details of each of the tasks we use to evaluate Perceiver IO here. The positional and task embeddings appended to inputs for each case are listed in Tab. 6.

Input: Table 6: Table best viewed on a screen. The structure and size of the positional and task embeddings used to construct Perceiver IO’s encoder key-value inputs and decoder query inputs, for each domain described in the main text. “[x, y]” indicates that x’s and y’s features are concatenated, while “x + y” indicates that x’s and y’s features are added to produce the full featurization. “FF” = Fourier features as in Jaegle et al. (2021).

Input: Figure 4: Multimodal audio-video-label autoencoding with 88x compression. Side-by-side: inputs on left, reconstructions right. See the supplemental material for example output video and audio.

Input: Figure 3: We construct queries with output-specific features to produce outputs with different semantics. For settings where each output point differs only in its position, like language, a position embedding can be used. Input features for the target output can also be used to query, either alone (as for StarCraft II) or alongside position features (as for flow). For multi-{task, modal} settings we use one embedding for each {task, modality} instead of each position. A single learned embedding suffices for simple classification tasks, like ImageNet. For tasks with heterogeneous outputs like multimodal autoencoding, features that are specific to some queries (like xy position) can be combined with modality embeddings, which also pad embeddings to fixed length.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Based on the extensive evidence provided, I'll evaluate the claim: "Our architecture can produce many outputs, each with arbitrary shape and structure, and yet the latent features in our architecture remain agnostic to the shape and structure of the outputs."

The evidence strongly supports this claim through multiple demonstrations:
- The architecture successfully handles diverse output types: text, video, audio, images, and game states
- The latent space is explicitly decoupled from input/output structures through the encode-process-decode architecture
- Figures and tables show concrete examples of handling varied output formats while maintaining the same core architecture
- The review comments acknowledge this as a key strength and innovation

The claim appears well-stated and accurately reflects the demonstrated capabilities without exaggeration. The evidence shows both the theoretical framework and practical results that validate this capability.

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Perceiver IO does this using a fully attentional read-process-write architecture: inputs are encoded (read) to a latent space, the latent representation is refined (process) via many layers of processing, and the latent space is decoded (write) to produce outputs.

The evidence to be evaluated is:Several groups have proposed to use attention to manipulate the size of arrays or to introduce bottlenecks in processing. Set Transformers and related work (Lee et al., 2019; Goyal et al., 2022) use a learned query ("inducing points") to induce local bottlenecks by mapping a set back and forth from a set with fewer elements and learned decoder queries ("seed vectors") to map to outputs ("pooling by multiheaded attention").

The Perceiver IO architecture builds on the Perceiver (Jaegle et al., 2021), which achieved its cross-domain generality by assuming that its input is a simple 2D byte array: a set of elements (which might be pixels or patches in vision, characters or words in language, or some form of embedding, learned or otherwise), each described by a feature vector. The model then encodes information about the input array using a smaller number of latent feature vectors, using Transformer-style attention, followed by iterative processing and a final aggregation down to a category label. Rather than output a single category, Perceiver IO aims to have the same level of generality with respect to its _outputs_ as the Perceiver has with respect to its _inputs_: that is, it should produce arbitrary output arrays. We can predict each element of the output array using another attention module by _querying_ the latent array using a query feature vector unique to the desired output element.

We first **encode** by applying an attention module that maps input arrays \(x\in\mathbb{R}^{M\times C}\) to arrays in a latent space \(z\in\mathbb{R}^{N\times D}\). We next **process** the latents \(z\) by applying a series of modules that take in and return arrays in this latent space. Finally, we **decode** by applying an attention module that maps latent arrays to output arrays \(y\in\mathbb{R}^{O\times E}\).

Following the design of the Perceiver, we implement each of the architecture's components using Transformer-style attention modules.

The Perceiver IO architecture builds on primitives similar to those in Transformers.

Perceiver IO uses attention non-homogeneously by mapping inputs to a latent space, processing in that latent space, and decoding to an output space. Perceiver IO has no quadratic dependence on the input or output size: encoder and decoder attention modules depend linearly on the input and output size (respectively), while latent attention is independent of both input and output sizes (Sec. E.2).

This architecture can be applied to inputs of any shape or spatial layout including inputs or outputs with different spatial structure (e.g. sound and video). In contrast to latent spaces typically used in vision (e.g. Ronneberger et al., 2015) the latent does not explicitly share the structure (spatial or otherwise) of the inputs. To decode this information, we query for it using cross-attention. Our goal is to produce a final output array of size \(O\times E\), given a latent representation of size \(N\times D\). We produce an output of this size by querying the decoder with an array of index dimension \(O\). To capture the structure of the output space, we use queries containing the appropriate information for each output point, e.g. its spatial position or its modality. We construct queries by combining (concatenating or adding) a set of vectors into a query vector containing all of the information relevant for one of the \(O\) desired outputs.

We find that even very simple query features can produce good results, suggesting that the latent attention process is able to learn to organize the relevant information in a way that's easy to query. Each output point depends only on its query and the latent array, allowing us to decode outputs in parallel.

Perceiver IO produces one output vector per masked input by using learnable position-dependent vectors to query the output of the final latent processing layer.

As a result of the reduced latent size of \(256\) we can train a much deeper network with 26 processing layers compared to BERT Base (12 layers) while maintaining a similar FLOPs budget.

In contrast, we apply Perceiver IO to flow in a straightforward manner.

We concatenate a fixed position encoding to these features and then apply Perceiver IO. To decode, we query the latent representation using the input encoding.

With Perceiver IO, we pad the inputs with modality-specific embeddings, serialize them into a single 2D input array and query outputs using queries containing position encodings (for video and audio) and modality embeddings.

We decode directly into pixels, raw audio, and the one-hot label without any post-processing.

In this work we introduce Perceiver IO, an architecture capable of handling general purpose inputs and outputs while scaling linearly in both input and output sizes.

Perceiver IO leverages the expressive power of latent attention and uses learned queries to expose a simple and unified interface that can handle multimodal and multitask settings.

These results demonstrate the unprecedented generality of Perceiver IO, the simplicity that this architecture unlocks in handling a range of tasks, and its flexibility to work as part of a domain-adapted system.

While neither the Perceiver and Perceiver IO incorporate any 2D spatial structure architecturally, they use positional features that inject 2D spatial information (Sec. 3.2 and Appendix sec. D of Jaegle et al. 2021).

We use an architecture with weight sharing in depth: the latent (processing) component of the architecture includes 8 blocks of 6 attention modules each, and weights are shared between the corresponding modules in each block.

Unlike in the other ImageNet experiments, we do not share weights in the latent self-attention process modules, but use a 16-layer latent network with no weight sharing in depth. Unlike the other ImageNet experiments, the process-module MLPs use a hidden layer with 4\(\times\) the number of channels (rather than 1\(\times\) as on other ImageNet experiments).

At each timestep, the architecture encodes units with an entity encoder, which in the original model was parameterized using a vanilla Transformer. The entity encoder takes as input a set of 512 entities (referred to as embedded_entity in Vinyals et al. (2019)) and produces as output an embedding for each entity (entity_embeddings) and a 1D embedding reduced over entities (embedded_entity).

We obtained StarCraft II results by using Perceiver IO instead of a Transformer for the AlphaStar entity encoder. We replaced the Transformer with a Perceiver IO with a latent of index dimension 32, keeping the input and output size of 512 units.

Perceiver IO is constructed from GPT-2-style (Radford et al., 2019) Transformer attention modules, which consist of QKV attention followed by an MLP, along with linear projection layers to ensure inputs to and outputs from the QKV attention and MLP take on desired sizes. Using the array sizes of the encoder attention, the QKV attention takes in two two-dimensional arrays, a key-value input array \(X_{KV}\in\mathbb{R}^{M\times C}\) and a query input array \(X_{Q}\in\mathbb{R}^{N\times D}\), and maps them to an array \(X_{QKV}\in\mathbb{R}^{N\times D}\), sharing the shape of the query input (after projection). \(X_{QKV}\) is used as input to an MLP, which is applied independently to each element of the index dimension (i.e., convolving the MLP with its input along the first dimension), producing a final array \(X_{MLP}\in\mathbb{R}^{N\times D}\).

Perceiver IO uses cross-attention for encoder and decoder attention modules and uses self-attention for the latent processing modules.

QKV attention takes in two two-dimensional arrays, a query input \(X_{Q}\in\mathbb{R}^{N\times D}\) and a key-value input \(X_{KV}\in\mathbb{R}^{M\times C}\).

\[Q=f_{Q}(X_{Q}); K=f_{K}(X_{KV}); V=f_{V}(X_{KV}) \tag{1}\] \[X_{QK}=\text{softmax}(QK^{T}/\sqrt{F})\] (2) \[\text{Attn}(X_{Q},X_{KV})=X_{QKV}=f_{O}(X_{QK}V), \tag{3}\] where \(X_{QK}\) is an array of attention maps \(\in\mathbb{R}^{N\times M}\), and \(X_{QKV}\) is an array \(\in\mathbb{R}^{N\times D}\). The functions \(f_{\{Q,K,V\}}\) are linear layers mapping each input to a shared feature dimension \(F\) and \(f_{O}\) is a linear layer projecting the output to a target channel dimension, which is often the same size as \(X_{Q}\)'s.

The full module has the following structure: \[X_{QKV} =\text{Attn}(\text{layerNorm}(X_{Q}),\text{layerNorm}(X_{KV})) \tag{4}\] \[X_{QKV} =X_{QKV}+X_{Q}\] (5) \[X_{QKV} =X_{QKV}+\text{MLP}(\text{layerNorm}(X_{QKV})), \tag{6}\]

The computational complexity of each attention module is dominated by the two matrix multiplications in QKV attention. Still using the shapes of the encoder attention, these two matrix multiplies involve matrices of shape \(M\times F\) and \(N\times F\) and \(M\times N\) and \(N\times F\), giving overall time and memory complexity of \(\mathcal{O}(MNF)\). Let \(M\), \(N\), and \(O\) be the index dimensions for the input, latent, and output arrays, and to simplify the analysis let \(F\) be the feature size for all layers.

In other words, Perceiver IO has complexity linear in the size of the input and output arrays and it decouples the depth of the latent transformer from the input and output sizes. Both of these properties contribute to Perceiver IO's efficiency: while many proposals for efficient attention modules or architectures include linear or sub-quadratic scaling with input/output size, Perceiver IO is unusual in also decoupling depth from input/output size (without requiring domain-specific strategies like 2D convolution).

Instead of uniformly weighting each input in the averaging operation, decoder attention uses the attention scores as data-dependent weights for each input point.

In order to decode, we use learned queries of the same dimension of the input array (Tab. 11). We have as many output queries as inputs to be able to predict the masked token at all positions in the sentence (\(M\)=\(O\)).

After feeding this through the Perceiver IO architecture (including querying with the same convolutional features used as input), we have an output a feature grid with stride 4 and 64 channels, on top of which we apply a RAFT upsampling layer.

We use a latent array with 2048 elements and 512 channels and 24 self-attention modules, each with 16 self-attention heads, unless otherwise noted.

For these experiments, the output of the perceiver decoder's cross attend is 64 channels, which is fed into a RAFT-style upsampling operation.

The decoder outputs \(16\times 224\times 224+16\times 1920/16+1\) vectors with 512 channels, that is, one element for each pixel in the video, one element for each audio patch, and one element for the classification label. These are then linearly projected to the appropriate channel size for each modality: 3 for videos, 16 for audio and 700 for classification (the logits for each of the 700 classes in Kinetics700). Finally, we un-patch the audio to arrive at the output audio. We note that we read and generate the audio waveform directly in the time domain; we do not transform first to a spectrogram. We use a 387 dimensional 3D Fourier position embedding for each input video patch and a 385 dimensional 1D Fourier position embedding for each audio patch (385 to ensure the input dimensions to Perceiver IO match for all elements). In addition, we pad all input elements with a learned vector representing the modality; inputs from the same modality share the same token. In particular, we add a 317 dimensional modality embedding to video elements, a 319 dimensional modality embedding to audio elements, and a 4 dimensional modality embedding to the label, so that all elements have 704 features. The decoder queries are also constructed from Fourier position embeddings for video and audio and a learned positional embedding for label: 387 features for video, 385 features for audio, and 1024 learned features for the label. We pad the queries for each modality with a different learned vector for each modality, so that the final feature size for the queries is 1026.

Perceiver IO is able to capture the structure of both video and audio inputs, despite encoding both jointly with a single network.

Although decoding can be done in parallel, allowing us to decode very large output arrays in sequential batches, Perceiver IO requires all points are encoded simultaneously.

Input: Figure 5: Schematic depiction of encode, process, and decode attention. Each attention module uses the same operations, but differs in which inputs are used to generate key/values or queries and in the output shape. Encode attention can be viewed as mapping an input to a latent space, typically with a smaller index dimension (fewer elements). Decode attention can be viewed as mapping a latent to an output space, often with a larger index dimension (more elements). Both of these are forms of cross-attention. Process attention (self-attention) preserves the input index dimension (same elements). Red and blue dashed lines are used to highlight the two matrix multiplications used in QKV attention, as described in the text.

Input: Figure 2: The Perceiver IO architecture. Perceiver IO maps arbitrary input arrays to arbitrary output arrays in a domain agnostic process. The bulk of the computation happens in a latent space whose size is typically smaller than the inputs and outputs, which makes the process computationally tractable even for very large inputs & outputs. See Fig. 5 for a more detailed look at encode, process, and decode attention.

Input: Figure 4: Multimodal audio-video-label autoencoding with 88x compression. Side-by-side: inputs on left, reconstructions right. See the supplemental material for example output video and audio.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that the claim accurately describes Perceiver IO's core architecture. The evidence, particularly Figures 2 and 5, clearly demonstrates the three-part "read-process-write" structure through encode (read), process, and decode (write) attention modules. The technical descriptions in the text explicitly confirm this architecture, detailing how inputs are encoded to a latent space (read), processed through multiple attention layers (process), and decoded to produce outputs (write). The claim is well-stated and directly supported by both visual and textual evidence without exaggeration.

<score>1</score>

Justification:
1. The evidence directly supports each component:
- "Read" (encode): "We first encode by applying an attention module that maps input arrays x∈ℝ^(M×C) to arrays in a latent space z∈ℝ^(N×D)"
- "Process": "We next process the latents z by applying a series of modules that take in and return arrays in this latent space"
- "Write" (decode): "Finally, we decode by applying an attention module that maps latent arrays to output arrays y∈ℝ^(O×E)"

2. The figures provide clear visual confirmation of this architecture and information flow.

3. The claim avoids overstating capabilities or performance, focusing solely on describing the fundamental architecture, which is thoroughly documented in the evidence.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Perceiver IO does this using a fully attentional read-process-write architecture: inputs are encoded (read) to a latent space, the latent representation is refined (process) via many layers of processing, and the latent space is decoded (write) to produce outputs.

The review comment to be evaluated is:

#### Strengths

- Major contribution is ability to scale the output to arbitrary length using an output query array. This enables supporting different types of output and scales well to different tasks.
- Byte level performance of Perceiver IO is impressive compared to BERT baseline. Byte level embeddings can be fed to Perceiver IO without any tokenization and this setting gets comparable performance with BERT sentence piece tokenization.
- Shows good results with multitask learning, using both single and task specific tokens.
- Authors do a thorough evaluation of this architecture on a wide variety of tasks, and achieves comparable results on multiple tasks in language, vision, multimodal, RL domains.

#### Weaknesses

- Novelty over Perceiver : Although I find this work quite impressive as it scales linearly with output embedding sizes as well as arbitrary types of outputs, the overall architecture seems incremental compared to Perceiver. One important change is there is no cross attention in the intermediate layers. I would love to see more detailed analysis why that is removed in this architecture and empirical evidence to support this change.

- How does choosing values for N,D affect the performance on various tasks. If we decouple input representation, output representation and base architecture, how does model performance scale with depth, N, D values.  Ablations on these hyper-parameters are required for more clarity.

- Given Perceiver can handle multiple modalities, I would be interested to see how it performs on multimodal tasks like VQA, Image/Text Retrieval, Hateful Memes etc and comparison to multimodal models like CLIP[1], ALIGN[2], SimVL[3].

  - [1] Radford, Alec, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry et al. "Learning transferable visual models from natural language supervision." arXiv preprint arXiv:2103.00020 (2021).
  - [2] Jia, Chao, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, and Tom Duerig. "Scaling up visual and vision-language representation learning with noisy text supervision." arXiv preprint arXiv:2102.05918 (2021).
  - [3] Wang, Zirui, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. "SimVLM: Simple visual language model pretraining with weak supervision." arXiv preprint arXiv:2108.10904 (2021).

The evidence to be evaluated is:Several groups have proposed to use attention to manipulate the size of arrays or to introduce bottlenecks in processing. Set Transformers and related work (Lee et al., 2019; Goyal et al., 2022) use a learned query ("inducing points") to induce local bottlenecks by mapping a set back and forth from a set with fewer elements and learned decoder queries ("seed vectors") to map to outputs ("pooling by multiheaded attention").

The Perceiver IO architecture builds on the Perceiver (Jaegle et al., 2021), which achieved its cross-domain generality by assuming that its input is a simple 2D byte array: a set of elements (which might be pixels or patches in vision, characters or words in language, or some form of embedding, learned or otherwise), each described by a feature vector. The model then encodes information about the input array using a smaller number of latent feature vectors, using Transformer-style attention, followed by iterative processing and a final aggregation down to a category label. Rather than output a single category, Perceiver IO aims to have the same level of generality with respect to its _outputs_ as the Perceiver has with respect to its _inputs_: that is, it should produce arbitrary output arrays. We can predict each element of the output array using another attention module by _querying_ the latent array using a query feature vector unique to the desired output element.

We first **encode** by applying an attention module that maps input arrays \(x\in\mathbb{R}^{M\times C}\) to arrays in a latent space \(z\in\mathbb{R}^{N\times D}\). We next **process** the latents \(z\) by applying a series of modules that take in and return arrays in this latent space. Finally, we **decode** by applying an attention module that maps latent arrays to output arrays \(y\in\mathbb{R}^{O\times E}\).

Following the design of the Perceiver, we implement each of the architecture's components using Transformer-style attention modules.

The Perceiver IO architecture builds on primitives similar to those in Transformers.

Perceiver IO uses attention non-homogeneously by mapping inputs to a latent space, processing in that latent space, and decoding to an output space. Perceiver IO has no quadratic dependence on the input or output size: encoder and decoder attention modules depend linearly on the input and output size (respectively), while latent attention is independent of both input and output sizes (Sec. E.2).

This architecture can be applied to inputs of any shape or spatial layout including inputs or outputs with different spatial structure (e.g. sound and video). In contrast to latent spaces typically used in vision (e.g. Ronneberger et al., 2015) the latent does not explicitly share the structure (spatial or otherwise) of the inputs. To decode this information, we query for it using cross-attention. Our goal is to produce a final output array of size \(O\times E\), given a latent representation of size \(N\times D\). We produce an output of this size by querying the decoder with an array of index dimension \(O\). To capture the structure of the output space, we use queries containing the appropriate information for each output point, e.g. its spatial position or its modality. We construct queries by combining (concatenating or adding) a set of vectors into a query vector containing all of the information relevant for one of the \(O\) desired outputs.

We find that even very simple query features can produce good results, suggesting that the latent attention process is able to learn to organize the relevant information in a way that's easy to query. Each output point depends only on its query and the latent array, allowing us to decode outputs in parallel.

Perceiver IO produces one output vector per masked input by using learnable position-dependent vectors to query the output of the final latent processing layer.

As a result of the reduced latent size of \(256\) we can train a much deeper network with 26 processing layers compared to BERT Base (12 layers) while maintaining a similar FLOPs budget.

In contrast, we apply Perceiver IO to flow in a straightforward manner.

We concatenate a fixed position encoding to these features and then apply Perceiver IO. To decode, we query the latent representation using the input encoding.

With Perceiver IO, we pad the inputs with modality-specific embeddings, serialize them into a single 2D input array and query outputs using queries containing position encodings (for video and audio) and modality embeddings.

We decode directly into pixels, raw audio, and the one-hot label without any post-processing.

In this work we introduce Perceiver IO, an architecture capable of handling general purpose inputs and outputs while scaling linearly in both input and output sizes.

Perceiver IO leverages the expressive power of latent attention and uses learned queries to expose a simple and unified interface that can handle multimodal and multitask settings.

These results demonstrate the unprecedented generality of Perceiver IO, the simplicity that this architecture unlocks in handling a range of tasks, and its flexibility to work as part of a domain-adapted system.

While neither the Perceiver and Perceiver IO incorporate any 2D spatial structure architecturally, they use positional features that inject 2D spatial information (Sec. 3.2 and Appendix sec. D of Jaegle et al. 2021).

We use an architecture with weight sharing in depth: the latent (processing) component of the architecture includes 8 blocks of 6 attention modules each, and weights are shared between the corresponding modules in each block.

Unlike in the other ImageNet experiments, we do not share weights in the latent self-attention process modules, but use a 16-layer latent network with no weight sharing in depth. Unlike the other ImageNet experiments, the process-module MLPs use a hidden layer with 4\(\times\) the number of channels (rather than 1\(\times\) as on other ImageNet experiments).

At each timestep, the architecture encodes units with an entity encoder, which in the original model was parameterized using a vanilla Transformer. The entity encoder takes as input a set of 512 entities (referred to as embedded_entity in Vinyals et al. (2019)) and produces as output an embedding for each entity (entity_embeddings) and a 1D embedding reduced over entities (embedded_entity).

We obtained StarCraft II results by using Perceiver IO instead of a Transformer for the AlphaStar entity encoder. We replaced the Transformer with a Perceiver IO with a latent of index dimension 32, keeping the input and output size of 512 units.

Perceiver IO is constructed from GPT-2-style (Radford et al., 2019) Transformer attention modules, which consist of QKV attention followed by an MLP, along with linear projection layers to ensure inputs to and outputs from the QKV attention and MLP take on desired sizes. Using the array sizes of the encoder attention, the QKV attention takes in two two-dimensional arrays, a key-value input array \(X_{KV}\in\mathbb{R}^{M\times C}\) and a query input array \(X_{Q}\in\mathbb{R}^{N\times D}\), and maps them to an array \(X_{QKV}\in\mathbb{R}^{N\times D}\), sharing the shape of the query input (after projection). \(X_{QKV}\) is used as input to an MLP, which is applied independently to each element of the index dimension (i.e., convolving the MLP with its input along the first dimension), producing a final array \(X_{MLP}\in\mathbb{R}^{N\times D}\).

Perceiver IO uses cross-attention for encoder and decoder attention modules and uses self-attention for the latent processing modules.

QKV attention takes in two two-dimensional arrays, a query input \(X_{Q}\in\mathbb{R}^{N\times D}\) and a key-value input \(X_{KV}\in\mathbb{R}^{M\times C}\).

\[Q=f_{Q}(X_{Q}); K=f_{K}(X_{KV}); V=f_{V}(X_{KV}) \tag{1}\] \[X_{QK}=\text{softmax}(QK^{T}/\sqrt{F})\] (2) \[\text{Attn}(X_{Q},X_{KV})=X_{QKV}=f_{O}(X_{QK}V), \tag{3}\] where \(X_{QK}\) is an array of attention maps \(\in\mathbb{R}^{N\times M}\), and \(X_{QKV}\) is an array \(\in\mathbb{R}^{N\times D}\). The functions \(f_{\{Q,K,V\}}\) are linear layers mapping each input to a shared feature dimension \(F\) and \(f_{O}\) is a linear layer projecting the output to a target channel dimension, which is often the same size as \(X_{Q}\)'s.

The full module has the following structure: \[X_{QKV} =\text{Attn}(\text{layerNorm}(X_{Q}),\text{layerNorm}(X_{KV})) \tag{4}\] \[X_{QKV} =X_{QKV}+X_{Q}\] (5) \[X_{QKV} =X_{QKV}+\text{MLP}(\text{layerNorm}(X_{QKV})), \tag{6}\]

The computational complexity of each attention module is dominated by the two matrix multiplications in QKV attention. Still using the shapes of the encoder attention, these two matrix multiplies involve matrices of shape \(M\times F\) and \(N\times F\) and \(M\times N\) and \(N\times F\), giving overall time and memory complexity of \(\mathcal{O}(MNF)\). Let \(M\), \(N\), and \(O\) be the index dimensions for the input, latent, and output arrays, and to simplify the analysis let \(F\) be the feature size for all layers.

In other words, Perceiver IO has complexity linear in the size of the input and output arrays and it decouples the depth of the latent transformer from the input and output sizes. Both of these properties contribute to Perceiver IO's efficiency: while many proposals for efficient attention modules or architectures include linear or sub-quadratic scaling with input/output size, Perceiver IO is unusual in also decoupling depth from input/output size (without requiring domain-specific strategies like 2D convolution).

Instead of uniformly weighting each input in the averaging operation, decoder attention uses the attention scores as data-dependent weights for each input point.

In order to decode, we use learned queries of the same dimension of the input array (Tab. 11). We have as many output queries as inputs to be able to predict the masked token at all positions in the sentence (\(M\)=\(O\)).

After feeding this through the Perceiver IO architecture (including querying with the same convolutional features used as input), we have an output a feature grid with stride 4 and 64 channels, on top of which we apply a RAFT upsampling layer.

We use a latent array with 2048 elements and 512 channels and 24 self-attention modules, each with 16 self-attention heads, unless otherwise noted.

For these experiments, the output of the perceiver decoder's cross attend is 64 channels, which is fed into a RAFT-style upsampling operation.

The decoder outputs \(16\times 224\times 224+16\times 1920/16+1\) vectors with 512 channels, that is, one element for each pixel in the video, one element for each audio patch, and one element for the classification label. These are then linearly projected to the appropriate channel size for each modality: 3 for videos, 16 for audio and 700 for classification (the logits for each of the 700 classes in Kinetics700). Finally, we un-patch the audio to arrive at the output audio. We note that we read and generate the audio waveform directly in the time domain; we do not transform first to a spectrogram. We use a 387 dimensional 3D Fourier position embedding for each input video patch and a 385 dimensional 1D Fourier position embedding for each audio patch (385 to ensure the input dimensions to Perceiver IO match for all elements). In addition, we pad all input elements with a learned vector representing the modality; inputs from the same modality share the same token. In particular, we add a 317 dimensional modality embedding to video elements, a 319 dimensional modality embedding to audio elements, and a 4 dimensional modality embedding to the label, so that all elements have 704 features. The decoder queries are also constructed from Fourier position embeddings for video and audio and a learned positional embedding for label: 387 features for video, 385 features for audio, and 1024 learned features for the label. We pad the queries for each modality with a different learned vector for each modality, so that the final feature size for the queries is 1026.

Perceiver IO is able to capture the structure of both video and audio inputs, despite encoding both jointly with a single network.

Although decoding can be done in parallel, allowing us to decode very large output arrays in sequential batches, Perceiver IO requires all points are encoded simultaneously.

Input: Figure 5: Schematic depiction of encode, process, and decode attention. Each attention module uses the same operations, but differs in which inputs are used to generate key/values or queries and in the output shape. Encode attention can be viewed as mapping an input to a latent space, typically with a smaller index dimension (fewer elements). Decode attention can be viewed as mapping a latent to an output space, often with a larger index dimension (more elements). Both of these are forms of cross-attention. Process attention (self-attention) preserves the input index dimension (same elements). Red and blue dashed lines are used to highlight the two matrix multiplications used in QKV attention, as described in the text.

Input: Figure 2: The Perceiver IO architecture. Perceiver IO maps arbitrary input arrays to arbitrary output arrays in a domain agnostic process. The bulk of the computation happens in a latent space whose size is typically smaller than the inputs and outputs, which makes the process computationally tractable even for very large inputs & outputs. See Fig. 5 for a more detailed look at encode, process, and decode attention.

Input: Figure 4: Multimodal audio-video-label autoencoding with 88x compression. Side-by-side: inputs on left, reconstructions right. See the supplemental material for example output video and audio.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, I find that the claim accurately describes Perceiver IO's core architectural approach of read-process-write using attention mechanisms. The evidence clearly shows:

1. Encoding (read) via attention that maps input arrays to latent space
2. Processing through multiple layers of latent attention
3. Decoding (write) via attention to produce outputs

The description aligns perfectly with Figure 2 and the detailed technical explanations provided. The claim avoids exaggeration and sticks to fundamental architectural facts that are well-supported by the evidence. The review comments also support this characterization while discussing additional details about scalability and performance.

<score>1</score>

The claim is well-stated and matches the evidence precisely without overselling or overgeneralizing the architecture's capabilities.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Perceiver IO does this using a fully attentional read-process-write architecture: inputs are encoded (read) to a latent space, the latent representation is refined (process) via many layers of processing, and the latent space is decoded (write) to produce outputs.

The review comment to be evaluated is:

The main strength of Perceiver IO is its high flexibility to various input domains. While keeping the main architecture, we can focus on engineering the input query preprocessing with domain-specific knowledge. Perceiver IO not only shows flexibility but also shows high performance. The results are comparable to or sometimes outperform previous domain-specific methods. For example

- Table 1 shows that Perceiver IO works well even without tokenization, while BERT + UTF-8 bytes show a significant performance drop (81.0 -> 71.5). It may show that Perceiver IO can perform well even without minimal domain knowledge (tokenization).
- Table 2 shows that the input query engineering can improve the target task (e.g., single-task query to 8 multi-task queries).
- Table 5 shows that the small input preprocessing engineering can improve the performance a lot. For example, Perceiver IO shows 82.1 top-1 accuracies for conv preprocessing while Perceiver IO with Fourier features shows 79.0.

I have minor concerns about ImageNet experiments.

- Although this paper is not aiming to state-of-the-art ImageNet performances, I think Perceiver IO is not yet comparable to state-of-the-art models on ImageNet image classification (as described in the last paragraph of Section 1). Perceiver IO still needs tremendous FLOPs compared to vision-specific models (Table 17). For example, ResNet shows 78.6 top-1 accuracy with 4.1 GFLOPs, while Perceiver IO Fourier shows 79.0 top-1 accuracy with 407 GFLOPs (99 times larger FLOPs). Stronger Perceiver IO (conv) shows 82.1 top-1 accuracy with 369 GFLOPs, where CaiT-M48 448 shows 86.5 top-1 accuracy and 329.6 GFLOPs.
- "Perceiver IO (pretrained)" and "Perceiver IO" in 2D Fourier features are different models. For example, "Perceiver IO (pretrained)" does not use weight sharing, and uses larger channel sizes. As a result, Perceiver IO (pretrained) has 4.38 times larger parameters compared to Perceiver IO (but half GFLOPs). The current text can make a reader mislead that "Perceiver IO (pretrained)" is the same model as "Perceiver IO", but the only difference is the JFT pretraining, which is not true.

> Unlike in the other ImageNet experiments, we do not share weights in the latent self-attention process modules, but use a 16-layer latent network with no weight sharing in depth. Unlike the other ImageNet experiments, the process-module MLPs use a hidden layer with 4× the number of channels (rather than 1× as on other ImageNet experiments)

To avoid confusion, I suggest replacing Table 5 with Table 17. Also, I would like to suggest to clarify that "Perceiver IO (pretrained)" and "Perceiver IO" are not the same model.

## Questions

- Perceiver IO shows better performances consistently compared to Perceiver IO baseline on GLUE (Table 13), Audio Set (Table 7), and ImageNet (Table 17). To my knowledge, there is no specific reason to the proposed output array cross-attention works better than the conventional classification head. I wonder what is the opinion by the authors what is the source of the improvements.
- In Table 5, I found that using 2D + maxpool preprocessing harms Perceiver performances (78.6 -> 77.4), but it helps Perceiver IO a lot (79.0 -> 82.1). As far as the reviewer understood, the main difference between Perceiver and Perceiver IO is the last decoding layer, not the input processing. I wonder what is the opinion of the authors why Perceiver and Perceiver IO show contradictory results on ImageNet Fourier features and conv preprocessing.
- I wonder why there is no Perceiver IO (conv) + pretraining result. Do the authors have any plan to add Perceiver IO (conv + pretrained)? (this is not a mandatory experiment for the rebuttal)

## Minor comments

- Many details are missing in the main text. To understand the method, the readers should read through the heavy appendix and the previous Perceiver paper. I fully understand that this is because of the page limitation, but this paper is somewhat hard to understand at first glance without any prior knowledge.
- In my opinion, it would be very helpful to readers if Table 8 and 9 are in the main text.
- pg 3. Typo -- unnecessary space *( "inducing points")*
- pg 29. Missing hyperlink (Tab. ??)
- In my opinion, it will be very interesting if there is one unified Perceiver model pretrained on various domain data. For example, one can train a Perceiver model trained by language and vision modalities, where each modality has different input and output arrays.

The evidence to be evaluated is:Several groups have proposed to use attention to manipulate the size of arrays or to introduce bottlenecks in processing. Set Transformers and related work (Lee et al., 2019; Goyal et al., 2022) use a learned query ("inducing points") to induce local bottlenecks by mapping a set back and forth from a set with fewer elements and learned decoder queries ("seed vectors") to map to outputs ("pooling by multiheaded attention").

The Perceiver IO architecture builds on the Perceiver (Jaegle et al., 2021), which achieved its cross-domain generality by assuming that its input is a simple 2D byte array: a set of elements (which might be pixels or patches in vision, characters or words in language, or some form of embedding, learned or otherwise), each described by a feature vector. The model then encodes information about the input array using a smaller number of latent feature vectors, using Transformer-style attention, followed by iterative processing and a final aggregation down to a category label. Rather than output a single category, Perceiver IO aims to have the same level of generality with respect to its _outputs_ as the Perceiver has with respect to its _inputs_: that is, it should produce arbitrary output arrays. We can predict each element of the output array using another attention module by _querying_ the latent array using a query feature vector unique to the desired output element.

We first **encode** by applying an attention module that maps input arrays \(x\in\mathbb{R}^{M\times C}\) to arrays in a latent space \(z\in\mathbb{R}^{N\times D}\). We next **process** the latents \(z\) by applying a series of modules that take in and return arrays in this latent space. Finally, we **decode** by applying an attention module that maps latent arrays to output arrays \(y\in\mathbb{R}^{O\times E}\).

Following the design of the Perceiver, we implement each of the architecture's components using Transformer-style attention modules.

The Perceiver IO architecture builds on primitives similar to those in Transformers.

Perceiver IO uses attention non-homogeneously by mapping inputs to a latent space, processing in that latent space, and decoding to an output space. Perceiver IO has no quadratic dependence on the input or output size: encoder and decoder attention modules depend linearly on the input and output size (respectively), while latent attention is independent of both input and output sizes (Sec. E.2).

This architecture can be applied to inputs of any shape or spatial layout including inputs or outputs with different spatial structure (e.g. sound and video). In contrast to latent spaces typically used in vision (e.g. Ronneberger et al., 2015) the latent does not explicitly share the structure (spatial or otherwise) of the inputs. To decode this information, we query for it using cross-attention. Our goal is to produce a final output array of size \(O\times E\), given a latent representation of size \(N\times D\). We produce an output of this size by querying the decoder with an array of index dimension \(O\). To capture the structure of the output space, we use queries containing the appropriate information for each output point, e.g. its spatial position or its modality. We construct queries by combining (concatenating or adding) a set of vectors into a query vector containing all of the information relevant for one of the \(O\) desired outputs.

We find that even very simple query features can produce good results, suggesting that the latent attention process is able to learn to organize the relevant information in a way that's easy to query. Each output point depends only on its query and the latent array, allowing us to decode outputs in parallel.

Perceiver IO produces one output vector per masked input by using learnable position-dependent vectors to query the output of the final latent processing layer.

As a result of the reduced latent size of \(256\) we can train a much deeper network with 26 processing layers compared to BERT Base (12 layers) while maintaining a similar FLOPs budget.

In contrast, we apply Perceiver IO to flow in a straightforward manner.

We concatenate a fixed position encoding to these features and then apply Perceiver IO. To decode, we query the latent representation using the input encoding.

With Perceiver IO, we pad the inputs with modality-specific embeddings, serialize them into a single 2D input array and query outputs using queries containing position encodings (for video and audio) and modality embeddings.

We decode directly into pixels, raw audio, and the one-hot label without any post-processing.

In this work we introduce Perceiver IO, an architecture capable of handling general purpose inputs and outputs while scaling linearly in both input and output sizes.

Perceiver IO leverages the expressive power of latent attention and uses learned queries to expose a simple and unified interface that can handle multimodal and multitask settings.

These results demonstrate the unprecedented generality of Perceiver IO, the simplicity that this architecture unlocks in handling a range of tasks, and its flexibility to work as part of a domain-adapted system.

While neither the Perceiver and Perceiver IO incorporate any 2D spatial structure architecturally, they use positional features that inject 2D spatial information (Sec. 3.2 and Appendix sec. D of Jaegle et al. 2021).

We use an architecture with weight sharing in depth: the latent (processing) component of the architecture includes 8 blocks of 6 attention modules each, and weights are shared between the corresponding modules in each block.

Unlike in the other ImageNet experiments, we do not share weights in the latent self-attention process modules, but use a 16-layer latent network with no weight sharing in depth. Unlike the other ImageNet experiments, the process-module MLPs use a hidden layer with 4\(\times\) the number of channels (rather than 1\(\times\) as on other ImageNet experiments).

At each timestep, the architecture encodes units with an entity encoder, which in the original model was parameterized using a vanilla Transformer. The entity encoder takes as input a set of 512 entities (referred to as embedded_entity in Vinyals et al. (2019)) and produces as output an embedding for each entity (entity_embeddings) and a 1D embedding reduced over entities (embedded_entity).

We obtained StarCraft II results by using Perceiver IO instead of a Transformer for the AlphaStar entity encoder. We replaced the Transformer with a Perceiver IO with a latent of index dimension 32, keeping the input and output size of 512 units.

Perceiver IO is constructed from GPT-2-style (Radford et al., 2019) Transformer attention modules, which consist of QKV attention followed by an MLP, along with linear projection layers to ensure inputs to and outputs from the QKV attention and MLP take on desired sizes. Using the array sizes of the encoder attention, the QKV attention takes in two two-dimensional arrays, a key-value input array \(X_{KV}\in\mathbb{R}^{M\times C}\) and a query input array \(X_{Q}\in\mathbb{R}^{N\times D}\), and maps them to an array \(X_{QKV}\in\mathbb{R}^{N\times D}\), sharing the shape of the query input (after projection). \(X_{QKV}\) is used as input to an MLP, which is applied independently to each element of the index dimension (i.e., convolving the MLP with its input along the first dimension), producing a final array \(X_{MLP}\in\mathbb{R}^{N\times D}\).

Perceiver IO uses cross-attention for encoder and decoder attention modules and uses self-attention for the latent processing modules.

QKV attention takes in two two-dimensional arrays, a query input \(X_{Q}\in\mathbb{R}^{N\times D}\) and a key-value input \(X_{KV}\in\mathbb{R}^{M\times C}\).

\[Q=f_{Q}(X_{Q}); K=f_{K}(X_{KV}); V=f_{V}(X_{KV}) \tag{1}\] \[X_{QK}=\text{softmax}(QK^{T}/\sqrt{F})\] (2) \[\text{Attn}(X_{Q},X_{KV})=X_{QKV}=f_{O}(X_{QK}V), \tag{3}\] where \(X_{QK}\) is an array of attention maps \(\in\mathbb{R}^{N\times M}\), and \(X_{QKV}\) is an array \(\in\mathbb{R}^{N\times D}\). The functions \(f_{\{Q,K,V\}}\) are linear layers mapping each input to a shared feature dimension \(F\) and \(f_{O}\) is a linear layer projecting the output to a target channel dimension, which is often the same size as \(X_{Q}\)'s.

The full module has the following structure: \[X_{QKV} =\text{Attn}(\text{layerNorm}(X_{Q}),\text{layerNorm}(X_{KV})) \tag{4}\] \[X_{QKV} =X_{QKV}+X_{Q}\] (5) \[X_{QKV} =X_{QKV}+\text{MLP}(\text{layerNorm}(X_{QKV})), \tag{6}\]

The computational complexity of each attention module is dominated by the two matrix multiplications in QKV attention. Still using the shapes of the encoder attention, these two matrix multiplies involve matrices of shape \(M\times F\) and \(N\times F\) and \(M\times N\) and \(N\times F\), giving overall time and memory complexity of \(\mathcal{O}(MNF)\). Let \(M\), \(N\), and \(O\) be the index dimensions for the input, latent, and output arrays, and to simplify the analysis let \(F\) be the feature size for all layers.

In other words, Perceiver IO has complexity linear in the size of the input and output arrays and it decouples the depth of the latent transformer from the input and output sizes. Both of these properties contribute to Perceiver IO's efficiency: while many proposals for efficient attention modules or architectures include linear or sub-quadratic scaling with input/output size, Perceiver IO is unusual in also decoupling depth from input/output size (without requiring domain-specific strategies like 2D convolution).

Instead of uniformly weighting each input in the averaging operation, decoder attention uses the attention scores as data-dependent weights for each input point.

In order to decode, we use learned queries of the same dimension of the input array (Tab. 11). We have as many output queries as inputs to be able to predict the masked token at all positions in the sentence (\(M\)=\(O\)).

After feeding this through the Perceiver IO architecture (including querying with the same convolutional features used as input), we have an output a feature grid with stride 4 and 64 channels, on top of which we apply a RAFT upsampling layer.

We use a latent array with 2048 elements and 512 channels and 24 self-attention modules, each with 16 self-attention heads, unless otherwise noted.

For these experiments, the output of the perceiver decoder's cross attend is 64 channels, which is fed into a RAFT-style upsampling operation.

The decoder outputs \(16\times 224\times 224+16\times 1920/16+1\) vectors with 512 channels, that is, one element for each pixel in the video, one element for each audio patch, and one element for the classification label. These are then linearly projected to the appropriate channel size for each modality: 3 for videos, 16 for audio and 700 for classification (the logits for each of the 700 classes in Kinetics700). Finally, we un-patch the audio to arrive at the output audio. We note that we read and generate the audio waveform directly in the time domain; we do not transform first to a spectrogram. We use a 387 dimensional 3D Fourier position embedding for each input video patch and a 385 dimensional 1D Fourier position embedding for each audio patch (385 to ensure the input dimensions to Perceiver IO match for all elements). In addition, we pad all input elements with a learned vector representing the modality; inputs from the same modality share the same token. In particular, we add a 317 dimensional modality embedding to video elements, a 319 dimensional modality embedding to audio elements, and a 4 dimensional modality embedding to the label, so that all elements have 704 features. The decoder queries are also constructed from Fourier position embeddings for video and audio and a learned positional embedding for label: 387 features for video, 385 features for audio, and 1024 learned features for the label. We pad the queries for each modality with a different learned vector for each modality, so that the final feature size for the queries is 1026.

Perceiver IO is able to capture the structure of both video and audio inputs, despite encoding both jointly with a single network.

Although decoding can be done in parallel, allowing us to decode very large output arrays in sequential batches, Perceiver IO requires all points are encoded simultaneously.

Input: Figure 5: Schematic depiction of encode, process, and decode attention. Each attention module uses the same operations, but differs in which inputs are used to generate key/values or queries and in the output shape. Encode attention can be viewed as mapping an input to a latent space, typically with a smaller index dimension (fewer elements). Decode attention can be viewed as mapping a latent to an output space, often with a larger index dimension (more elements). Both of these are forms of cross-attention. Process attention (self-attention) preserves the input index dimension (same elements). Red and blue dashed lines are used to highlight the two matrix multiplications used in QKV attention, as described in the text.

Input: Figure 2: The Perceiver IO architecture. Perceiver IO maps arbitrary input arrays to arbitrary output arrays in a domain agnostic process. The bulk of the computation happens in a latent space whose size is typically smaller than the inputs and outputs, which makes the process computationally tractable even for very large inputs & outputs. See Fig. 5 for a more detailed look at encode, process, and decode attention.

Input: Figure 4: Multimodal audio-video-label autoencoding with 88x compression. Side-by-side: inputs on left, reconstructions right. See the supplemental material for example output video and audio.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim, evidence, and review comments, I'll assess the accuracy of the claim about Perceiver IO's read-process-write architecture.

Summary: The evidence strongly supports the claim's description of Perceiver IO's architecture. Figures 2 and 5 explicitly show the three-part architecture (encode/read, process, decode/write) described in the claim. The technical details in the text evidence confirm this structure, explaining how inputs are encoded to a latent space through attention, processed through multiple layers, and decoded to outputs. The claim is straightforward and accurately reflects the core architectural design without exaggeration.

<score>1</score>

Justification:
1. Direct evidence supports each component:
- Read/Encode: "We first encode by applying an attention module that maps input arrays x∈ℝMxC to arrays in a latent space z∈ℝNxD"
- Process: "We next process the latents z by applying a series of modules that take in and return arrays in this latent space"
- Write/Decode: "Finally, we decode by applying an attention module that maps latent arrays to output arrays y∈ℝOxE"

2. The figures provide clear visual confirmation of this architecture.

3. The claim makes no exaggerated statements about capabilities or performance - it simply describes the fundamental architecture accurately.

4. The review comments do not contradict this architectural description, focusing instead on performance considerations.

This claim is well-stated (score of 1) as it precisely describes the core architecture without overstating its capabilities or achievements.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Perceiver IO does this using a fully attentional read-process-write architecture: inputs are encoded (read) to a latent space, the latent representation is refined (process) via many layers of processing, and the latent space is decoded (write) to produce outputs.

The review comment to be evaluated is:

Pros:

1. This paper introduced a generic architecture for coping with different tasks with various input and output lengths. It is different from Transformer architecture and Perceiver architecture in that it uses a latent process to compress the larger number of input tokens into a hidden space, and then decode the output from this hidden space. This way it can decouple the inputs and outputs which has the potential to significantly reduce the cost brought by a large number of input and output tokens.

2. The authors presented a thorough study of Perceiver IO across different application scenarios, such as language, vision, multi-modality. Extensive experiments demonstrate that the proposed method can achieve comparable or even better performance than the baselines. To facilitate the adaptation, the authors suggested a number of good ways to convert the inputs/outputs into certain formats.

Cons:

1. The authors should list the sizes of inputs, latent, and output arrays for each of the downstream tasks to give the audience a better sense of the complexity. For some of the tasks, such as ImageNet classification, the authors should also report the FLOPs in comparisons with prior arts, e.g., ViTs.

2. The main merit of Perceiver IO compared with Perceiver is that it further introduces a cross-attention-based output decoder on top of the latent arrays. Though Perceiver was originally proposed for classification tasks, it can be also used to cope with structured outputs. For some of the tasks except for ImageNet classification listed in this paper, I am curious whether Perceiver can be applied and how it performs compared with Perceiver IO.

3. Perceiver IO is proposed as a generic architecture for various tasks by modeling them as a read-process-write process. One question is that whether we should reformulate all these tasks as the way shown in this paper. This paper does show some encouraging results on various tasks compared with the baselines. However, they are only compared with baseline methods and still underperforms established methods in specific domains. As we know, different tasks can still benefit a lot from the specific domain knowledge (e.g., 2D spatial for image recognition). Actually, we can also think of the Transformer encoder as a generic architecture in numerous domains. Then, do we need to build up a higher-level generic architecture upon the Transformer encoder?

4. A good part of unifying architectures for various tasks is that we can train the same set of parameters using different tasks. In this paper, the authors demonstrated the effectiveness of Perceiver IO across different settings, I am wondering whether such a generic architecture can be used for multi-task training so that it can leverage the training data from different tasks.

The evidence to be evaluated is:Several groups have proposed to use attention to manipulate the size of arrays or to introduce bottlenecks in processing. Set Transformers and related work (Lee et al., 2019; Goyal et al., 2022) use a learned query ("inducing points") to induce local bottlenecks by mapping a set back and forth from a set with fewer elements and learned decoder queries ("seed vectors") to map to outputs ("pooling by multiheaded attention").

The Perceiver IO architecture builds on the Perceiver (Jaegle et al., 2021), which achieved its cross-domain generality by assuming that its input is a simple 2D byte array: a set of elements (which might be pixels or patches in vision, characters or words in language, or some form of embedding, learned or otherwise), each described by a feature vector. The model then encodes information about the input array using a smaller number of latent feature vectors, using Transformer-style attention, followed by iterative processing and a final aggregation down to a category label. Rather than output a single category, Perceiver IO aims to have the same level of generality with respect to its _outputs_ as the Perceiver has with respect to its _inputs_: that is, it should produce arbitrary output arrays. We can predict each element of the output array using another attention module by _querying_ the latent array using a query feature vector unique to the desired output element.

We first **encode** by applying an attention module that maps input arrays \(x\in\mathbb{R}^{M\times C}\) to arrays in a latent space \(z\in\mathbb{R}^{N\times D}\). We next **process** the latents \(z\) by applying a series of modules that take in and return arrays in this latent space. Finally, we **decode** by applying an attention module that maps latent arrays to output arrays \(y\in\mathbb{R}^{O\times E}\).

Following the design of the Perceiver, we implement each of the architecture's components using Transformer-style attention modules.

The Perceiver IO architecture builds on primitives similar to those in Transformers.

Perceiver IO uses attention non-homogeneously by mapping inputs to a latent space, processing in that latent space, and decoding to an output space. Perceiver IO has no quadratic dependence on the input or output size: encoder and decoder attention modules depend linearly on the input and output size (respectively), while latent attention is independent of both input and output sizes (Sec. E.2).

This architecture can be applied to inputs of any shape or spatial layout including inputs or outputs with different spatial structure (e.g. sound and video). In contrast to latent spaces typically used in vision (e.g. Ronneberger et al., 2015) the latent does not explicitly share the structure (spatial or otherwise) of the inputs. To decode this information, we query for it using cross-attention. Our goal is to produce a final output array of size \(O\times E\), given a latent representation of size \(N\times D\). We produce an output of this size by querying the decoder with an array of index dimension \(O\). To capture the structure of the output space, we use queries containing the appropriate information for each output point, e.g. its spatial position or its modality. We construct queries by combining (concatenating or adding) a set of vectors into a query vector containing all of the information relevant for one of the \(O\) desired outputs.

We find that even very simple query features can produce good results, suggesting that the latent attention process is able to learn to organize the relevant information in a way that's easy to query. Each output point depends only on its query and the latent array, allowing us to decode outputs in parallel.

Perceiver IO produces one output vector per masked input by using learnable position-dependent vectors to query the output of the final latent processing layer.

As a result of the reduced latent size of \(256\) we can train a much deeper network with 26 processing layers compared to BERT Base (12 layers) while maintaining a similar FLOPs budget.

In contrast, we apply Perceiver IO to flow in a straightforward manner.

We concatenate a fixed position encoding to these features and then apply Perceiver IO. To decode, we query the latent representation using the input encoding.

With Perceiver IO, we pad the inputs with modality-specific embeddings, serialize them into a single 2D input array and query outputs using queries containing position encodings (for video and audio) and modality embeddings.

We decode directly into pixels, raw audio, and the one-hot label without any post-processing.

In this work we introduce Perceiver IO, an architecture capable of handling general purpose inputs and outputs while scaling linearly in both input and output sizes.

Perceiver IO leverages the expressive power of latent attention and uses learned queries to expose a simple and unified interface that can handle multimodal and multitask settings.

These results demonstrate the unprecedented generality of Perceiver IO, the simplicity that this architecture unlocks in handling a range of tasks, and its flexibility to work as part of a domain-adapted system.

While neither the Perceiver and Perceiver IO incorporate any 2D spatial structure architecturally, they use positional features that inject 2D spatial information (Sec. 3.2 and Appendix sec. D of Jaegle et al. 2021).

We use an architecture with weight sharing in depth: the latent (processing) component of the architecture includes 8 blocks of 6 attention modules each, and weights are shared between the corresponding modules in each block.

Unlike in the other ImageNet experiments, we do not share weights in the latent self-attention process modules, but use a 16-layer latent network with no weight sharing in depth. Unlike the other ImageNet experiments, the process-module MLPs use a hidden layer with 4\(\times\) the number of channels (rather than 1\(\times\) as on other ImageNet experiments).

At each timestep, the architecture encodes units with an entity encoder, which in the original model was parameterized using a vanilla Transformer. The entity encoder takes as input a set of 512 entities (referred to as embedded_entity in Vinyals et al. (2019)) and produces as output an embedding for each entity (entity_embeddings) and a 1D embedding reduced over entities (embedded_entity).

We obtained StarCraft II results by using Perceiver IO instead of a Transformer for the AlphaStar entity encoder. We replaced the Transformer with a Perceiver IO with a latent of index dimension 32, keeping the input and output size of 512 units.

Perceiver IO is constructed from GPT-2-style (Radford et al., 2019) Transformer attention modules, which consist of QKV attention followed by an MLP, along with linear projection layers to ensure inputs to and outputs from the QKV attention and MLP take on desired sizes. Using the array sizes of the encoder attention, the QKV attention takes in two two-dimensional arrays, a key-value input array \(X_{KV}\in\mathbb{R}^{M\times C}\) and a query input array \(X_{Q}\in\mathbb{R}^{N\times D}\), and maps them to an array \(X_{QKV}\in\mathbb{R}^{N\times D}\), sharing the shape of the query input (after projection). \(X_{QKV}\) is used as input to an MLP, which is applied independently to each element of the index dimension (i.e., convolving the MLP with its input along the first dimension), producing a final array \(X_{MLP}\in\mathbb{R}^{N\times D}\).

Perceiver IO uses cross-attention for encoder and decoder attention modules and uses self-attention for the latent processing modules.

QKV attention takes in two two-dimensional arrays, a query input \(X_{Q}\in\mathbb{R}^{N\times D}\) and a key-value input \(X_{KV}\in\mathbb{R}^{M\times C}\).

\[Q=f_{Q}(X_{Q}); K=f_{K}(X_{KV}); V=f_{V}(X_{KV}) \tag{1}\] \[X_{QK}=\text{softmax}(QK^{T}/\sqrt{F})\] (2) \[\text{Attn}(X_{Q},X_{KV})=X_{QKV}=f_{O}(X_{QK}V), \tag{3}\] where \(X_{QK}\) is an array of attention maps \(\in\mathbb{R}^{N\times M}\), and \(X_{QKV}\) is an array \(\in\mathbb{R}^{N\times D}\). The functions \(f_{\{Q,K,V\}}\) are linear layers mapping each input to a shared feature dimension \(F\) and \(f_{O}\) is a linear layer projecting the output to a target channel dimension, which is often the same size as \(X_{Q}\)'s.

The full module has the following structure: \[X_{QKV} =\text{Attn}(\text{layerNorm}(X_{Q}),\text{layerNorm}(X_{KV})) \tag{4}\] \[X_{QKV} =X_{QKV}+X_{Q}\] (5) \[X_{QKV} =X_{QKV}+\text{MLP}(\text{layerNorm}(X_{QKV})), \tag{6}\]

The computational complexity of each attention module is dominated by the two matrix multiplications in QKV attention. Still using the shapes of the encoder attention, these two matrix multiplies involve matrices of shape \(M\times F\) and \(N\times F\) and \(M\times N\) and \(N\times F\), giving overall time and memory complexity of \(\mathcal{O}(MNF)\). Let \(M\), \(N\), and \(O\) be the index dimensions for the input, latent, and output arrays, and to simplify the analysis let \(F\) be the feature size for all layers.

In other words, Perceiver IO has complexity linear in the size of the input and output arrays and it decouples the depth of the latent transformer from the input and output sizes. Both of these properties contribute to Perceiver IO's efficiency: while many proposals for efficient attention modules or architectures include linear or sub-quadratic scaling with input/output size, Perceiver IO is unusual in also decoupling depth from input/output size (without requiring domain-specific strategies like 2D convolution).

Instead of uniformly weighting each input in the averaging operation, decoder attention uses the attention scores as data-dependent weights for each input point.

In order to decode, we use learned queries of the same dimension of the input array (Tab. 11). We have as many output queries as inputs to be able to predict the masked token at all positions in the sentence (\(M\)=\(O\)).

After feeding this through the Perceiver IO architecture (including querying with the same convolutional features used as input), we have an output a feature grid with stride 4 and 64 channels, on top of which we apply a RAFT upsampling layer.

We use a latent array with 2048 elements and 512 channels and 24 self-attention modules, each with 16 self-attention heads, unless otherwise noted.

For these experiments, the output of the perceiver decoder's cross attend is 64 channels, which is fed into a RAFT-style upsampling operation.

The decoder outputs \(16\times 224\times 224+16\times 1920/16+1\) vectors with 512 channels, that is, one element for each pixel in the video, one element for each audio patch, and one element for the classification label. These are then linearly projected to the appropriate channel size for each modality: 3 for videos, 16 for audio and 700 for classification (the logits for each of the 700 classes in Kinetics700). Finally, we un-patch the audio to arrive at the output audio. We note that we read and generate the audio waveform directly in the time domain; we do not transform first to a spectrogram. We use a 387 dimensional 3D Fourier position embedding for each input video patch and a 385 dimensional 1D Fourier position embedding for each audio patch (385 to ensure the input dimensions to Perceiver IO match for all elements). In addition, we pad all input elements with a learned vector representing the modality; inputs from the same modality share the same token. In particular, we add a 317 dimensional modality embedding to video elements, a 319 dimensional modality embedding to audio elements, and a 4 dimensional modality embedding to the label, so that all elements have 704 features. The decoder queries are also constructed from Fourier position embeddings for video and audio and a learned positional embedding for label: 387 features for video, 385 features for audio, and 1024 learned features for the label. We pad the queries for each modality with a different learned vector for each modality, so that the final feature size for the queries is 1026.

Perceiver IO is able to capture the structure of both video and audio inputs, despite encoding both jointly with a single network.

Although decoding can be done in parallel, allowing us to decode very large output arrays in sequential batches, Perceiver IO requires all points are encoded simultaneously.

Input: Figure 5: Schematic depiction of encode, process, and decode attention. Each attention module uses the same operations, but differs in which inputs are used to generate key/values or queries and in the output shape. Encode attention can be viewed as mapping an input to a latent space, typically with a smaller index dimension (fewer elements). Decode attention can be viewed as mapping a latent to an output space, often with a larger index dimension (more elements). Both of these are forms of cross-attention. Process attention (self-attention) preserves the input index dimension (same elements). Red and blue dashed lines are used to highlight the two matrix multiplications used in QKV attention, as described in the text.

Input: Figure 2: The Perceiver IO architecture. Perceiver IO maps arbitrary input arrays to arbitrary output arrays in a domain agnostic process. The bulk of the computation happens in a latent space whose size is typically smaller than the inputs and outputs, which makes the process computationally tractable even for very large inputs & outputs. See Fig. 5 for a more detailed look at encode, process, and decode attention.

Input: Figure 4: Multimodal audio-video-label autoencoding with 88x compression. Side-by-side: inputs on left, reconstructions right. See the supplemental material for example output video and audio.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the claim and evidence, I find that the description of Perceiver IO's read-process-write architecture is well-stated and accurately reflects the evidence. The figures and text clearly demonstrate:

1. The encode (read) step using cross-attention to map input arrays to latent space
2. The process step with multiple layers of self-attention in latent space
3. The decode (write) step using cross-attention to map latent arrays to output arrays

The architectural diagrams (Figures 2 and 5) explicitly show these three components, and the technical descriptions in the text provide detailed mathematical formulations that align with the claim. The claim avoids overstatement by simply describing the core architectural components without making excessive claims about performance or capabilities.

<score>1</score>