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:

This approach allows us to decouple the size of elements used for the bulk of the computation (the latent) from the size of the input and output spaces, while making minimal assumptions about the spatial or locality structure of the input and output.

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"). Each layer of these networks has complexity linear in the input size, while Perceivers use a deep latent network with complexity independent of the input and output.

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 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.

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.

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.

Although Transformers were originally developed for language, their quadratic complexity makes them difficult to use on language inputs without tokenization, which typically shortens the length of input sequences by a factor of \(\sim\)4. 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.

As a token contains many bytes on average, we need to increase the sequence length to input a similar amount of text: we use input sequence lengths of \(512\) SentencePiece tokens or \(2048\) UTF-8 bytes.

For the byte-level models, the vocabulary size is much smaller: 256 bytes and 4 special tokens ([PAD], [MASK], [CLS], [SEP]).

We first observe that Perceiver IO applied on SentencePiece tokenized input sequences slightly outperforms a strong BERT baseline applied on the same inputs (81.2 vs 81.1). 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.

Next, we show that we can leverage Perceiver IO to run on much longer sequences than a regular Transformer. Rather than using a fixed, handcrafted vocabulary, our model works directly with the raw byte inputs: we simply feed in and predict the UTF-8 bytes of the input string.

Clark et al. (2022) also uses a bottleneck architecture to scale to longer text inputs, but their upsampling strategy differs from ours: they concatenate raw inputs with their aligned downsampled latent representation, apply a 1D convolution and then run a shallow transformer stack on the resulting upsampled sequence.

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).

To decode, we query the latent representation using the input encoding.

With traditional autoencoding models like convolutional encoder-decoders, it is not obvious how to combine these modalities, because each uses data of different dimensions - 3D (video), 1D (raw audio), and 0D (class labels) - and with wildly different numbers of elements. 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 used a latent array with 512 channels and 784, 392, and 196 latents, resulting in compression ratios of 88x, 176x, and 352x respectively.

Latent variables are shared across modalities, so the quality of reconstructions for each modality is sensitive to the weight of its loss term and other training hyperparameters.

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. Overall, Perceiver IO offers a promising way to simplify the construction of sophisticated neural pipelines and facilitate progress on multimodal and multitask problems.

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. For these experiments, we additionally use a 1D convolutional network to project the RGB at each point to 256 before concatenating it with the learned positional encoding.

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.

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).

In other image settings discussed here, we optionally use simple pre- and post-processing steps to reduce the size of very large inputs and outputs.

Incorporating a single convolution + max pooling leads to a moderate improvement in the performance of the architecture: this is perhaps unsurprising, as convolutional pre-processing injects information about the 2D structure of images into the architecture. By comparison ViT first processes images by applying a 2D convolution with matched kernel and stride to downsample its inputs (referred to as a "linear projection of flattened patches" in that work and throughout the ViT literature).

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.

We replaced this Transformer with a 3-layer Perceiver IO with a latent of index dimension 32. We tuned only the size of the index dimension (sweeping values of 32 and 64), but otherwise used the same hyperparameters as ImageNet.

All models use an architecture with 12 processor modules and a latent index dimension \(N\) of 512 (we omit the repeated cross-attends used in Jaegle et al. (2021)).

For all four model settings, we swept the number of latent channels (using \(D\in\{512,\,1024\}\)) and report the best value for each setting.

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 \(X_{MLP}\in\mathbb{R}^{N\times D}\).

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.

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.

A model with \(L\) latent attention blocks has complexity \(\mathcal{O}([M+O+LN]NF)\). 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.

For the BERT (matching FLOPs) model trained on bytes, we reduce the model width from \(768\) to \(512\), the feed-forward hidden size from \(3072\) to \(2048\), the number of layers from \(12\) to \(6\) and the number of attention heads from \(12\) to \(8\). Given the longer sequence length of \(2048\) bytes, this model has about the same number of inference FLOPs as a BERT Base model on a sequence length of \(512\) tokens.

For a given FLOPs budget, there is a trade off between the number of latents \(N\) and the width \(D\) of the latents. We ablate this in Tab. 15 by varying the number of latents between 128, 256 (best), and 512. We adapt the latent dimension accordingly to match the FLOPs budget.

\(1{\times}1\) means that only a single pixel (or pair of pixels) is used for each input element. 'Separate frames' means that the frames are not concatenated, but rather, input array elements are extracted independently from the two frames (thereby doubling the number of input elements). In the case of separate frames, \(1\times 1\) means essentially no preprocessing: each pixel becomes its own element with no spatio-temporal context whatsoever. We also performed experiments with a less expensive input model which uses a \(7\times 7\) convolution to 64 channels, followed by a max pool, similar to the one used in our ImageNet experiments. 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 then upsample this flow for a given pixel in the high-resolution flow map by applying attention over a neighboring 3x3 block of the low-resolution flow map, following the uppsampling approach in RAFT (Teed & Deng, 2020).

Convolutional downsampling and RAFT upsampling provide even more spatial context for both the input features and the queries, but this doesn't make up for the loss of resolution and overall performs slightly worse than using the full resolution.

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.

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).

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

this may be partially attributable to the preprocessing, which included coarse patching (Tab. 5) for both modalities due to the very high computational cost of processing raw video and audio inputs (which amount to over 2 million raw points).

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 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 6: Single-query attention decoder (left), as used in Perceiver IO for classification tasks and a standard average + project decoder (right), as used in Jaegle et al. (2021). Both modules can be seen as first aggregating latents by weighted averaging (learned, data-dependent weighting for the attention decoder; uniform weights for the average + project decoder) and then projecting to an output channel dimension (linear value projection + MLP for the attention decoder; simple linear projection by the average + project decoder). Attentional decoding is more expressive than average + project decoding and follows the same architectural template as encoder and processor modules.

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:
After reviewing the evidence, this claim appears to be well-stated. The evidence demonstrates that Perceiver IO successfully decouples input/output sizes from the latent computation through:

1. Clear mathematical formulation showing linear scaling with input/output size while keeping latent processing independent of both (O([M+O+LN]NF) complexity)
2. Empirical validation across diverse tasks with varying input/output dimensions (language, vision, audio, etc.)
3. Minimal assumptions about spatial structure, demonstrated by achieving good results even without domain-specific architectures
4. Flexibility in handling arbitrary input/output arrays through the query-based decoder design

The reviewer comments also support this, particularly noting how the architecture effectively decouples inputs/outputs to reduce computational costs. The claim accurately reflects the evidence without exaggeration.

<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:

This approach allows us to decouple the size of elements used for the bulk of the computation (the latent) from the size of the input and output spaces, while making minimal assumptions about the spatial or locality structure of the input and output.

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: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"). Each layer of these networks has complexity linear in the input size, while Perceivers use a deep latent network with complexity independent of the input and output.

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 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.

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.

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.

Although Transformers were originally developed for language, their quadratic complexity makes them difficult to use on language inputs without tokenization, which typically shortens the length of input sequences by a factor of \(\sim\)4. 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.

As a token contains many bytes on average, we need to increase the sequence length to input a similar amount of text: we use input sequence lengths of \(512\) SentencePiece tokens or \(2048\) UTF-8 bytes.

For the byte-level models, the vocabulary size is much smaller: 256 bytes and 4 special tokens ([PAD], [MASK], [CLS], [SEP]).

We first observe that Perceiver IO applied on SentencePiece tokenized input sequences slightly outperforms a strong BERT baseline applied on the same inputs (81.2 vs 81.1). 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.

Next, we show that we can leverage Perceiver IO to run on much longer sequences than a regular Transformer. Rather than using a fixed, handcrafted vocabulary, our model works directly with the raw byte inputs: we simply feed in and predict the UTF-8 bytes of the input string.

Clark et al. (2022) also uses a bottleneck architecture to scale to longer text inputs, but their upsampling strategy differs from ours: they concatenate raw inputs with their aligned downsampled latent representation, apply a 1D convolution and then run a shallow transformer stack on the resulting upsampled sequence.

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).

To decode, we query the latent representation using the input encoding.

With traditional autoencoding models like convolutional encoder-decoders, it is not obvious how to combine these modalities, because each uses data of different dimensions - 3D (video), 1D (raw audio), and 0D (class labels) - and with wildly different numbers of elements. 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 used a latent array with 512 channels and 784, 392, and 196 latents, resulting in compression ratios of 88x, 176x, and 352x respectively.

Latent variables are shared across modalities, so the quality of reconstructions for each modality is sensitive to the weight of its loss term and other training hyperparameters.

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. Overall, Perceiver IO offers a promising way to simplify the construction of sophisticated neural pipelines and facilitate progress on multimodal and multitask problems.

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. For these experiments, we additionally use a 1D convolutional network to project the RGB at each point to 256 before concatenating it with the learned positional encoding.

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.

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).

In other image settings discussed here, we optionally use simple pre- and post-processing steps to reduce the size of very large inputs and outputs.

Incorporating a single convolution + max pooling leads to a moderate improvement in the performance of the architecture: this is perhaps unsurprising, as convolutional pre-processing injects information about the 2D structure of images into the architecture. By comparison ViT first processes images by applying a 2D convolution with matched kernel and stride to downsample its inputs (referred to as a "linear projection of flattened patches" in that work and throughout the ViT literature).

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.

We replaced this Transformer with a 3-layer Perceiver IO with a latent of index dimension 32. We tuned only the size of the index dimension (sweeping values of 32 and 64), but otherwise used the same hyperparameters as ImageNet.

All models use an architecture with 12 processor modules and a latent index dimension \(N\) of 512 (we omit the repeated cross-attends used in Jaegle et al. (2021)).

For all four model settings, we swept the number of latent channels (using \(D\in\{512,\,1024\}\)) and report the best value for each setting.

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 \(X_{MLP}\in\mathbb{R}^{N\times D}\).

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.

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.

A model with \(L\) latent attention blocks has complexity \(\mathcal{O}([M+O+LN]NF)\). 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.

For the BERT (matching FLOPs) model trained on bytes, we reduce the model width from \(768\) to \(512\), the feed-forward hidden size from \(3072\) to \(2048\), the number of layers from \(12\) to \(6\) and the number of attention heads from \(12\) to \(8\). Given the longer sequence length of \(2048\) bytes, this model has about the same number of inference FLOPs as a BERT Base model on a sequence length of \(512\) tokens.

For a given FLOPs budget, there is a trade off between the number of latents \(N\) and the width \(D\) of the latents. We ablate this in Tab. 15 by varying the number of latents between 128, 256 (best), and 512. We adapt the latent dimension accordingly to match the FLOPs budget.

\(1{\times}1\) means that only a single pixel (or pair of pixels) is used for each input element. 'Separate frames' means that the frames are not concatenated, but rather, input array elements are extracted independently from the two frames (thereby doubling the number of input elements). In the case of separate frames, \(1\times 1\) means essentially no preprocessing: each pixel becomes its own element with no spatio-temporal context whatsoever. We also performed experiments with a less expensive input model which uses a \(7\times 7\) convolution to 64 channels, followed by a max pool, similar to the one used in our ImageNet experiments. 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 then upsample this flow for a given pixel in the high-resolution flow map by applying attention over a neighboring 3x3 block of the low-resolution flow map, following the uppsampling approach in RAFT (Teed & Deng, 2020).

Convolutional downsampling and RAFT upsampling provide even more spatial context for both the input features and the queries, but this doesn't make up for the loss of resolution and overall performs slightly worse than using the full resolution.

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.

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).

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

this may be partially attributable to the preprocessing, which included coarse patching (Tab. 5) for both modalities due to the very high computational cost of processing raw video and audio inputs (which amount to over 2 million raw points).

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 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 6: Single-query attention decoder (left), as used in Perceiver IO for classification tasks and a standard average + project decoder (right), as used in Jaegle et al. (2021). Both modules can be seen as first aggregating latents by weighted averaging (learned, data-dependent weighting for the attention decoder; uniform weights for the average + project decoder) and then projecting to an output channel dimension (linear value projection + MLP for the attention decoder; simple linear projection by the average + project decoder). Attentional decoding is more expressive than average + project decoding and follows the same architectural template as encoder and processor modules.

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:
After reviewing the evidence, the claim appears to be well-stated and accurately reflects the architecture's capabilities. The evidence demonstrates that Perceiver IO successfully decouples computation size from input/output dimensions through its latent space design while maintaining minimal assumptions about spatial structure. This is supported by:

1. The model's ability to handle diverse input/output formats (text, video, audio, images) without domain-specific architectures
2. Mathematical analysis showing linear scaling with input/output size
3. Successful results across multiple domains using the same core architecture
4. Explicit demonstrations of handling inputs with different spatial structures (1D audio, 2D images, 3D video)

The claim's scope matches what is demonstrated in the evidence without overreaching.

<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's decoding procedure uses an attention mechanism to map from latents to arbitrarily sized and structured outputs using a querying system that can flexibly specify the semantics needed for outputs on a wide range of domains, including dense and multitask settings.

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").

Several works use attention to process latent spaces that interface with input/output data using task- or domain-specific architectures (Carion et al., 2020; Locatello et al., 2020; Wang et al., 2021), and cross-attention itself is widely used to produce outputs in of a different size or structure from inputs (Dai et al., 2019; Desai and Johnson, 2021; Miech et al., 2021; Vaswani et al., 2017; Raffel et al., 2020; Santoro et al., 2018; Hudson and Zitnick, 2021; Ma et al., 2021). 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. In other words, we define a query array with the same number of elements as the desired output. The queries may be hand-designed, learned embeddings, or a simple function of the input. They attend to the latents to yield an output array of the desired shape.

Finally, we **decode** by applying an attention module that maps latent arrays to output arrays \(y\in\mathbb{R}^{O\times E}\).

Each of these modules applies a global query-key-value (QKV) attention operation followed by a multi-layer perceptron (MLP).

Both encoder and decoder take in two input arrays, the first used as input to the module's key and value networks, and the second used as input to the module's query network. The module's output has the same index dimension (the same number of elements) as the query input.

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).

To decode this information, we query for it using cross-attention.

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 illustrate the query structure for the tasks we consider here in Fig. 3. For tasks with simple outputs, such as classification, these queries can be reused for every example and can be learned from scratch. 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. For outputs with a multi-task or multimodal structure, we learn a single query for each task or for each modality: this information allows the network to distinguish one task or modality query from the others, much as positional encodings allow attention to distinguish one position from another. For other tasks, the output should reflect the content of the input at the query location: for instance, for flow we find it helpful to include the input feature at the point being queried, and for StarCraft II we use the unit information to associate the model's output with the corresponding unit. 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. This property allows us to amortize model training on datasets of very large output size.

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 first observe that Perceiver IO applied on SentencePiece tokenized input sequences slightly outperforms a strong BERT baseline applied on the same inputs (81.2 vs 81.1).

**Multitask Perceiver IO.** We use multitask queries as described in Sec. 3.2 to finetune on all 8 GLUE tasks simultaneously using the UTF-8 byte model (results in Tab. 2).

We observe that our multitask approach outperforms single-task approaches and matches the approach that uses 8 task-specific input tokens. Our approach is more generic as it decouples the output array from the input array by not relying on [CLS] tokens.

To decode, we query the latent representation using the input encoding.

We explore using Perceiver IO for audio-video-label multimodal autoencoding on the Kinetics-700-2020 dataset (Smaira et al., 2020). The goal of multimodal autoencoding is to learn a model that can accurately reconstruct multimodal inputs in the the presence of a bottleneck induced by an architecture.

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.

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

(1) on ImageNet, Perceiver IO surpasses 80% top-1 accuracy (84.5% top-1) without using 2D convolutions after pretraining on JFT.

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. Overall, Perceiver IO offers a promising way to simplify the construction of sophisticated neural pipelines and facilitate progress on multimodal and multitask problems.

For ease of reference and comparison across domains, we describe the input and output size and processing used in all experiments in Tab. 5 and provide details of input key/value, position encoding, and output queries used in all experiments in Tab. 6. On all domains but StarCraft II, we include experiments with several input configurations, ranging from no domain adaptation (e.g. tokenizer-free language, flow from raw pixels, ImageNet with no convolutional or patch-based preprocessing and fully learned position encodings) to moderate domain adaptation (e.g. SentencePiece language understanding, flow from conv+maxpool-processed images and with RAFT upsampling, ImageNet with conv+maxpool-preprocessing and 2D Fourier features). 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.

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.

At its core, AlphaStar represents the units in the game as a discrete, unordered set of symbols (the "units"). These units are represented by a vector of properties including unit type, position, and health. 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). These 512 entities represent the units and other entities that are present in the game: unused entity slots are masked. entity_embeddings is produced by passing the outputs of the entity encoder through a ReLU and a 1D convolution with 256 channels.

For this reason, we view AlphaStar as an important test case for Perceiver IO's ability to function as a general-purpose tool for processing symbolic or set-valued data. If the question is "can Perceiver IO serve as a replacement for a well-tuned Transformer as a symbolic processing engine?" then the answer is yes: 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.

We seek to confirm that the the attention-based decoder helps even on classification, where the original Perceiver's decoder could be used.

As in the ImageNet experiments, We compare the performance of Perceiver and Perceiver IO using models that are matched except for the decoder (we use an average + project decoder for Perceiver and a query-based attention decoder for Perceiver IO, see Sec. E.3 and Fig. 6).

We find that as in the ImageNet experiments, using the attention-based decoder leads to small but consistent improvements over the less generally applicable average + project decoder. Because Perceiver IO introduces no domain assumptions not present in the original Perceiver, this is evidence that Perceiver IO is a strictly more general model.

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).

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.

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.

\[X_{QKV} =\text{Attn}(\text{layerNorm}(X_{Q}),\text{layerNorm}(X_{KV})) \tag{4}\] \[X_{QKV} =X_{QKV}+X_{Q}\] (5) \[X_{QKV} =X_{QKV}+\text{MLP}(\text{layerNorm}(X_{QKV})), \tag{6}\]

"Attn" refers to QKV as described above.

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).

As we show in ImageNet and AudioSet experiments, the attentional decoder used here can be used in settings where standard average + project decoders are applicable. We find that the attentional decoder typically produces somewhat better results than the standard decoder. This likely occurs because attentional decoding is more expressive than average + project decoding.

Both decoders can be viewed as first averaging thelations and then projecting them to a target shape, but decoder attention uses more expressive modules for each of these operations. Instead of uniformly weighting each input in the averaging operation, decoder attention uses the attention scores as data-dependent weights for each input point. Instead of projecting the raw averaged input to a target dimensionality, decoder attention first projects inputs via a value layer and then processes them with an MLP. 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.

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 (\(M\)=\(O\)). To get an insight into the learnt queries we visualize the attention weights in the first cross attention layer on a small paragraph (Fig. 7). We discover that the model has learnt both position and content based look-ups. The position-based look-ups can be either very sparse and precise or more distributed and periodic. This second mode appears somewhat less often and is more efficient because more data is being attended to at the same time, but also more distributed, since the values are subsequently averaged:

For single-task experiments, we do not require a [CLS] token as we use a single decoding query vector. In both single-task and multi-task experiments an extra 2-layer MLP with a hidden size of \(E\) and a tanh activation is used to map the the Perceiver IO outputs to the class logits (or regression target for STS-B).

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.

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 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 6: Single-query attention decoder (left), as used in Perceiver IO for classification tasks and a standard average + project decoder (right), as used in Jaegle et al. (2021). Both modules can be seen as first aggregating latents by weighted averaging (learned, data-dependent weighting for the attention decoder; uniform weights for the average + project decoder) and then projecting to an output channel dimension (linear value projection + MLP for the attention decoder; simple linear projection by the average + project decoder). Attentional decoding is more expressive than average + project decoding and follows the same architectural template as encoder and processor modules.

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:
I've carefully reviewed the claim and evidence. The evidence strongly supports Perceiver IO's use of attention-based decoding to flexibly map latents to various output structures. The paper provides extensive technical details, mathematical formulations, and experimental validation across multiple domains (language, vision, audio, games) demonstrating how the querying system works to specify output semantics. The evidence shows concrete implementations and results that align well with the claim's scope. The claim accurately reflects the capabilities without exaggeration or overstatement.

<score>1</score>

Justification:
- Technical implementation details match claim's description (Figs 2,3,5)
- Demonstrated success across diverse domains validates flexibility claim
- Mathematical formulation provided for attention-based decoding
- Detailed experimental results support effectiveness
- Clear explanation of query construction for different output types
- No exaggerated language or unsupported assertions
- Claims scope aligns with presented evidence