LookupViT: Compressing visual information to a limited number of tokens
(2407.12753)Abstract
Vision Transformers (ViT) have emerged as the de-facto choice for numerous industry grade vision solutions. But their inference cost can be prohibitive for many settings, as they compute self-attention in each layer which suffers from quadratic computational complexity in the number of tokens. On the other hand, spatial information in images and spatio-temporal information in videos is usually sparse and redundant. In this work, we introduce LookupViT, that aims to exploit this information sparsity to reduce ViT inference cost. LookupViT provides a novel general purpose vision transformer block that operates by compressing information from higher resolution tokens to a fixed number of tokens. These few compressed tokens undergo meticulous processing, while the higher-resolution tokens are passed through computationally cheaper layers. Information sharing between these two token sets is enabled through a bidirectional cross-attention mechanism. The approach offers multiple advantages - (a) easy to implement on standard ML accelerators (GPUs/TPUs) via standard high-level operators, (b) applicable to standard ViT and its variants, thus generalizes to various tasks, (c) can handle different tokenization and attention approaches. LookupViT also offers flexibility for the compressed tokens, enabling performance-computation trade-offs in a single trained model. We show LookupViT's effectiveness on multiple domains - (a) for image-classification (ImageNet-1K and ImageNet-21K), (b) video classification (Kinetics400 and Something-Something V2), (c) image captioning (COCO-Captions) with a frozen encoder. LookupViT provides $2\times$ reduction in FLOPs while upholding or improving accuracy across these domains. In addition, LookupViT also demonstrates out-of-the-box robustness and generalization on image classification (ImageNet-C,R,A,O), improving by up to $4\%$ over ViT.
Overview
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LookupViT introduces a novel Vision Transformer architecture that reduces computational costs by compressing tokens using a multi-head bidirectional cross-attention mechanism.
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The architecture demonstrates superior performance and efficiency across various tasks, including image and video classification, by effectively managing computational complexity.
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The paper outlines multiple avenues for future research, including extending the approach to dense prediction tasks, exploring larger model configurations, and assessing its efficacy in domain adaptation and transfer learning.
A Comprehensive Overview of LookupViT: Compressing Visual Information to a Limited Number of Tokens
This essay explore the paper titled "LookupViT: Compressing visual information to a limited number of tokens," which presents a novel approach to efficient visual information processing using Vision Transformers (ViT). The persistent challenge addressed is the high computational cost associated with standard ViTs, predominantly due to their quadratic complexity in handling tokens through self-attention mechanisms. The proposed LookupViT architecture effectively mitigates this issue by exploiting sparsity and redundancy in visual data, thereby significantly reducing inference costs while maintaining, or even enhancing, performance across various domains.
Key Methodological Contributions
Efficient Token Compression: LookupViT introduces an innovative Vision Transformer block that reduces higher-resolution tokens into a fixed number of compressed tokens. This compression is achieved through a multi-head bidirectional cross-attention mechanism, ensuring effective information exchange between compressed and lookup tokens.
Bidirectional Cross-Attention Mechanism: The core of LookupViT’s architecture is its novel multi-head bidirectional cross-attention (MHBC) module. This module facilitates information flow from lookup tokens to compressed tokens (MHBC({l\rightarrow p})) and vice versa (MHBC({p\rightarrow l})). The compressed tokens undergo computationally intensive operations while the lookup tokens are processed through comparatively lighter operations, thus managing computational complexity effectively.
Flexibility and Scalability: LookupViT is adaptable to varying model configurations and can efficiently manage different tokenization and attention strategies. The multi-resolution capability allows training a single model with varying compressed token resolutions, thus enabling a performance-computation trade-off during inference with the same parameter space.
Performance Evaluation
Image Classification
Experiments on standard benchmarks (ImageNet-1K and ImageNet-21K) demonstrate notable performance improvements. LookupViT achieves a 2x reduction in FLOPs, maintaining or improving accuracy. For instance, LookupViT$_{10\times10}$ exhibits a 1.6% accuracy improvement over ViT on ImageNet-1K while requiring fewer computational resources.
Robustness and Generalization
LookupViT shows enhanced robustness and generalization capabilities. Evaluations on corrupted and out-of-distribution datasets (ImageNet-C, ImageNet-A, ImageNet-R, ImageNet-O) highlight LookupViT’s superior performance over standard ViT models. The paper’s analysis indicates that LookupViT maintains lower deviations in feature representations under adversarial conditions, underscoring its robustness.
Video Classification and Captioning
When extended to video classification, LookupViT demonstrates competitive performance on Kinetics400 and strong improvements on Something-Something V2 (SSv2), showcasing its efficacy in handling complex spatio-temporal data. For image captioning (COCO-Captions), LookupViT maintains high performance with frozen encoders, outperforming other token compression methods like TokenLearner.
Theoretical and Practical Implications
The theoretical contributions of LookupViT extend beyond its immediate application, offering insights into efficient model design for vision tasks. The flexible architecture accommodates different scales and resolutions, promoting efficiency without sacrificing accuracy. This has profound implications for deploying vision models in resource-constrained environments, where computational efficiency is paramount.
Future Directions
The promising results presented in this paper suggest several avenues for future research:
Extension to Dense Prediction Tasks:
Extending LookupViT to tasks like object detection and semantic segmentation could validate its applicability across a broader range of vision tasks.
Larger Model Sizes and Architectures:
Exploring the performance and scalability of LookupViT with larger models and diverse architectures could further enhance its robustness and versatility.
Domain Adaptation and Transfer Learning:
Investigating LookupViT’s efficacy in domain adaptation and transfer learning scenarios could open new opportunities for cross-domain applications.
Conclusion
LookupViT represents a significant stride towards efficient vision processing. By intelligently compressing tokens and utilizing a bidirectional cross-attention mechanism, it achieves a commendable balance between performance and computational cost. The comprehensive evaluation across multiple domains, coupled with the robust performance metrics, underscores its potential as a flexible and scalable solution for contemporary vision tasks.
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