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:

In this work, we develop a mechanism for decoding structured outputs - language, optical flow fields, audiovisual sequences, symbolic unordered sets, etc. - directly from the Perceiver latent space, which allows the model to handle a host of new domains without sacrificing the benefits of deep, domain-agnostic processing.

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.

Several groups have demonstrated that Transformers (originally designed for language) can be used or adapted to non-language tasks (e.g. Chen et al. 2020; Lu et al. 2021), but the limited scalability of Transformers limits their usefulness as general-purpose architectures.

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

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. Each of these modules applies a global query-key-value (QKV) attention operation followed by a multi-layer perceptron (MLP). As usual in Transformer-style architectures, we apply the MLP independently to each element of the index dimension.

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).

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

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. 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.

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). 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.

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. This is especially appealing when the tasks are many or inhomogeneous, as we show in Sec. 4.3.

This has many broader applications, such as navigation and visual odometry in robots (Campbell et al., 2004), estimation of 3D geometry (Ranftl et al., 2020), and even to aid transfer of more complex, learned inference such as 3D human pose estimation from synthetic to real images (Doersch and Zisserman, 2019).

To generalize to real data, the learned procedure needs to work for objects and textures that weren't seen in the training data.

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). 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 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.

Due to the scale of inputs and outputs in this task we subsample decoding in training, while fully decoding in testing: we sampled 512 audio samples and 512 pixels and the class label for every training example. This allows us to directly decode to a video-sized array, which would otherwise be infeasible given memory constraints.

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.

(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.

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. Perceiver did well on ImageNet (Deng et al., 2009) classification without using 2D structure in the design of the architecture, but generated class scores using a simple average + project decoder (see Sec. E.3 and Fig. 6 for a diagram illustrating the difference between the two forms of decoder).

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.

To further demonstrate Perceiver IO's capabilities on discrete modalities and as a drop-in replacement for Transformers, we plug in Perceiver IO in place of AlphaStar's Transformer. AlphaStar (Vinyals et al., 2019) is the state-of-the-art system for the challenging real-time strategy game of StarCraft II. 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.

Perceiver IO works out of the box. We observed that the resulting agent reached the same level of performance as the original AlphaStar agent, reaching an 87% win-rate versus the Elite bot after behavioral cloning (Pomerleau, 1989) on human data, while also leading to a 3\(\times\) decrease in FLOPs (Tab. 9). We replaced this Transformer with a 3-layer Perceiver IO with a latent of index dimension 32.

We show that the trend identified on ImageNet holds more generally, by revisiting the multimodal AudioSet classification domain.

We compare models taking video and either raw audio or mel-spectrogram (pre-processed audio) as input.

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.

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.

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.

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.

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:

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. This involves a linear projection from 64 dimensions to 2, which is the coarse-resolution optical flow estimate.

Finally, to verify that Perceiver IO performs well on real-world data despite being trained only on synthetic imagery, we applied it to a small number (roughly 10) real videos taken from Getty images (www.gettyimages.com). Perceiver IO typically performs very well out-of-domain, although some failure cases remain: for instance, shadows tend to be interpreted as objects (Autoflow contains no shadows), and large regions with compression artifacts but no other texture may result in hallucinated flow (Autoflow contains no video compression artifacts). We include three challenging examples in the supplementary zip file, each of which depict complex motion and small objects. Perceiver IO can pick up on remarkably small objects such as the water droplets thrown by the girl's shoe in pigeon.mp4 or the confetti in thai_dance.mp4.

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 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.

To help verify the quality of Perceiver IO's outputs on real-world data, we applied it a small number of real videos (\(\sim\)10) with audio taken from Getty Images. 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 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:
After reviewing the claim and extensive evidence provided, I find that the claim accurately reflects the capabilities demonstrated by Perceiver IO across multiple domains. The evidence shows successful implementations in language modeling, optical flow, audio-visual processing, game states, and other domains - all using the same core architecture. The paper provides detailed experimental results, architecture schematics, and qualitative examples that substantiate the claim about developing a mechanism for decoding structured outputs directly from the latent space. The limitations and constraints (like computational costs for raw inputs) are also appropriately acknowledged. The claim is well-stated without exaggeration.

<score>1</score>

Justification: The claim is supported by:
1. Detailed experimental results across diverse domains showing successful implementation
2. Clear architectural diagrams and descriptions of how inputs/outputs are handled
3. Specific performance metrics and comparisons to baselines
4. Transparent discussion of limitations and trade-offs
5. Visual evidence of results on real-world data
6. Mathematical formulation of the approach
7. Comprehensive ablation studies validating the approach

The evidence fully supports the scope and specificity of the claim without overreaching.


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:

In this work, we develop a mechanism for decoding structured outputs - language, optical flow fields, audiovisual sequences, symbolic unordered sets, etc. - directly from the Perceiver latent space, which allows the model to handle a host of new domains without sacrificing the benefits of deep, domain-agnostic processing.

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: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.

Several groups have demonstrated that Transformers (originally designed for language) can be used or adapted to non-language tasks (e.g. Chen et al. 2020; Lu et al. 2021), but the limited scalability of Transformers limits their usefulness as general-purpose architectures.

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

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. Each of these modules applies a global query-key-value (QKV) attention operation followed by a multi-layer perceptron (MLP). As usual in Transformer-style architectures, we apply the MLP independently to each element of the index dimension.

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).

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

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. 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.

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). 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.

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. This is especially appealing when the tasks are many or inhomogeneous, as we show in Sec. 4.3.

This has many broader applications, such as navigation and visual odometry in robots (Campbell et al., 2004), estimation of 3D geometry (Ranftl et al., 2020), and even to aid transfer of more complex, learned inference such as 3D human pose estimation from synthetic to real images (Doersch and Zisserman, 2019).

To generalize to real data, the learned procedure needs to work for objects and textures that weren't seen in the training data.

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). 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 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.

Due to the scale of inputs and outputs in this task we subsample decoding in training, while fully decoding in testing: we sampled 512 audio samples and 512 pixels and the class label for every training example. This allows us to directly decode to a video-sized array, which would otherwise be infeasible given memory constraints.

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.

(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.

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. Perceiver did well on ImageNet (Deng et al., 2009) classification without using 2D structure in the design of the architecture, but generated class scores using a simple average + project decoder (see Sec. E.3 and Fig. 6 for a diagram illustrating the difference between the two forms of decoder).

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.

To further demonstrate Perceiver IO's capabilities on discrete modalities and as a drop-in replacement for Transformers, we plug in Perceiver IO in place of AlphaStar's Transformer. AlphaStar (Vinyals et al., 2019) is the state-of-the-art system for the challenging real-time strategy game of StarCraft II. 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.

Perceiver IO works out of the box. We observed that the resulting agent reached the same level of performance as the original AlphaStar agent, reaching an 87% win-rate versus the Elite bot after behavioral cloning (Pomerleau, 1989) on human data, while also leading to a 3\(\times\) decrease in FLOPs (Tab. 9). We replaced this Transformer with a 3-layer Perceiver IO with a latent of index dimension 32.

We show that the trend identified on ImageNet holds more generally, by revisiting the multimodal AudioSet classification domain.

We compare models taking video and either raw audio or mel-spectrogram (pre-processed audio) as input.

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.

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.

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.

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.

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:

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. This involves a linear projection from 64 dimensions to 2, which is the coarse-resolution optical flow estimate.

Finally, to verify that Perceiver IO performs well on real-world data despite being trained only on synthetic imagery, we applied it to a small number (roughly 10) real videos taken from Getty images (www.gettyimages.com). Perceiver IO typically performs very well out-of-domain, although some failure cases remain: for instance, shadows tend to be interpreted as objects (Autoflow contains no shadows), and large regions with compression artifacts but no other texture may result in hallucinated flow (Autoflow contains no video compression artifacts). We include three challenging examples in the supplementary zip file, each of which depict complex motion and small objects. Perceiver IO can pick up on remarkably small objects such as the water droplets thrown by the girl's shoe in pigeon.mp4 or the confetti in thai_dance.mp4.

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 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.

To help verify the quality of Perceiver IO's outputs on real-world data, we applied it a small number of real videos (\(\sim\)10) with audio taken from Getty Images. 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 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:
After careful review, I find that the claim is generally well-stated with strong supporting evidence. The paper demonstrates the architecture's capability to handle diverse structured outputs through multiple experiments across language, vision, audio, and symbolic domains. The evidence shows successful implementations in masked language modeling, optical flow fields, audio-visual sequences, and StarCraft II symbolic sets. The results validate the claim's core assertion about developing a general mechanism for decoding structured outputs from the Perceiver latent space. The limitations and trade-offs are also appropriately acknowledged in the evidence.

<score>1</score>

Justification:
- The claim directly aligns with extensive experimental evidence spanning multiple domains
- Results demonstrate successful handling of various output types as claimed (language, optical flow, audiovisual, symbolic)
- Evidence provides detailed technical explanations and results supporting the mechanism's functionality
- Authors acknowledge limitations and requirements for different implementations
- The review comments, while noting some incremental aspects, support the core capability claims
- The claim avoids exaggeration and accurately reflects the demonstrated 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:

In this work, we develop a mechanism for decoding structured outputs - language, optical flow fields, audiovisual sequences, symbolic unordered sets, etc. - directly from the Perceiver latent space, which allows the model to handle a host of new domains without sacrificing the benefits of deep, domain-agnostic processing.

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: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.

Several groups have demonstrated that Transformers (originally designed for language) can be used or adapted to non-language tasks (e.g. Chen et al. 2020; Lu et al. 2021), but the limited scalability of Transformers limits their usefulness as general-purpose architectures.

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

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. Each of these modules applies a global query-key-value (QKV) attention operation followed by a multi-layer perceptron (MLP). As usual in Transformer-style architectures, we apply the MLP independently to each element of the index dimension.

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).

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

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. 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.

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). 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.

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. This is especially appealing when the tasks are many or inhomogeneous, as we show in Sec. 4.3.

This has many broader applications, such as navigation and visual odometry in robots (Campbell et al., 2004), estimation of 3D geometry (Ranftl et al., 2020), and even to aid transfer of more complex, learned inference such as 3D human pose estimation from synthetic to real images (Doersch and Zisserman, 2019).

To generalize to real data, the learned procedure needs to work for objects and textures that weren't seen in the training data.

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). 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 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.

Due to the scale of inputs and outputs in this task we subsample decoding in training, while fully decoding in testing: we sampled 512 audio samples and 512 pixels and the class label for every training example. This allows us to directly decode to a video-sized array, which would otherwise be infeasible given memory constraints.

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.

(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.

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. Perceiver did well on ImageNet (Deng et al., 2009) classification without using 2D structure in the design of the architecture, but generated class scores using a simple average + project decoder (see Sec. E.3 and Fig. 6 for a diagram illustrating the difference between the two forms of decoder).

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.

To further demonstrate Perceiver IO's capabilities on discrete modalities and as a drop-in replacement for Transformers, we plug in Perceiver IO in place of AlphaStar's Transformer. AlphaStar (Vinyals et al., 2019) is the state-of-the-art system for the challenging real-time strategy game of StarCraft II. 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.

Perceiver IO works out of the box. We observed that the resulting agent reached the same level of performance as the original AlphaStar agent, reaching an 87% win-rate versus the Elite bot after behavioral cloning (Pomerleau, 1989) on human data, while also leading to a 3\(\times\) decrease in FLOPs (Tab. 9). We replaced this Transformer with a 3-layer Perceiver IO with a latent of index dimension 32.

We show that the trend identified on ImageNet holds more generally, by revisiting the multimodal AudioSet classification domain.

We compare models taking video and either raw audio or mel-spectrogram (pre-processed audio) as input.

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.

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.

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.

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.

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:

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. This involves a linear projection from 64 dimensions to 2, which is the coarse-resolution optical flow estimate.

Finally, to verify that Perceiver IO performs well on real-world data despite being trained only on synthetic imagery, we applied it to a small number (roughly 10) real videos taken from Getty images (www.gettyimages.com). Perceiver IO typically performs very well out-of-domain, although some failure cases remain: for instance, shadows tend to be interpreted as objects (Autoflow contains no shadows), and large regions with compression artifacts but no other texture may result in hallucinated flow (Autoflow contains no video compression artifacts). We include three challenging examples in the supplementary zip file, each of which depict complex motion and small objects. Perceiver IO can pick up on remarkably small objects such as the water droplets thrown by the girl's shoe in pigeon.mp4 or the confetti in thai_dance.mp4.

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 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.

To help verify the quality of Perceiver IO's outputs on real-world data, we applied it a small number of real videos (\(\sim\)10) with audio taken from Getty Images. 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 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:
After reviewing the evidence, the claim appears to be well-stated (1) as it accurately reflects the capabilities and implementation of Perceiver IO without exaggeration. The evidence demonstrates that:

1. The architecture successfully handles diverse structured outputs (language, flow fields, audio-visual, symbolic sets) through a unified decoding mechanism using query-based cross-attention.
2. The approach maintains computational efficiency through linear scaling.
3. Results across multiple domains validate the effectiveness of direct decoding from latent space.
4. The reviewer comments support the claim's validity while noting some limitations in specific domains like ImageNet efficiency.

The claim carefully describes the core mechanism without overstating its capabilities or performance.

<score>1</score>