AdaViT: Adaptive Vision Transformers for Efficient Image Recognition (2111.15668v1)

Abstract: Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of vision tasks recently. While achieving excellent performance, they still require relatively intensive computational cost that scales up drastically as the numbers of patches, self-attention heads and transformer blocks increase. In this paper, we argue that due to the large variations among images, their need for modeling long-range dependencies between patches differ. To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition. Optimized jointly with a transformer backbone in an end-to-end manner, a light-weight decision network is attached to the backbone to produce decisions on-the-fly. Extensive experiments on ImageNet demonstrate that our method obtains more than 2x improvement on efficiency compared to state-of-the-art vision transformers with only 0.8% drop of accuracy, achieving good efficiency/accuracy trade-offs conditioned on different computational budgets. We further conduct quantitative and qualitative analysis on learned usage polices and provide more insights on the redundancy in vision transformers.

An Exploration of AdaViT for Adaptive Vision Transformer Efficiency

The paper "AdaViT: Adaptive Vision Transformers for Efficient Image Recognition" presents an advanced framework aimed at enhancing the operational efficiency of vision transformers, which have become notable for their competitive performance in various computer vision tasks. Although these models harness the power of self-attention mechanisms inherent to transformers to achieve superior results in tasks ranging from image classification to object detection, their computational costs often escalate with the complexity of the architecture—including the number of patches, attention heads, and transformer blocks.

Overview of AdaViT

AdaViT—short for Adaptive Vision Transformer—addresses the problem of inefficiency in vision transformers by leveraging the variation in image complexities. The foundational hypothesis is that different images require varying levels of computational resources to capture long-range dependencies effectively. AdaViT introduces an adaptive computation framework that learns to derive specific usage policies for each input image. These policies determine the necessary patches, self-attention heads, and transformer blocks that should be activated at a given time, thereby optimizing the computational cost dynamically while maintaining a minimal impact on accuracy.

At the core of AdaViT is a light-weight decision network integrated with the transformer backbone. This network, optimized in an end-to-end learning setup, makes on-the-fly decisions regarding the resources to be utilized for processing a specific image. The framework’s efficacy has been demonstrated with extensive experimentation on the ImageNet dataset, where it achieved more than a twofold improvement in efficiency alongside a mere 0.8% drop in accuracy compared to the state-of-the-art static vision transformers.

Technical Insights

AdaViT extends the standard design of vision transformers by incorporating a decision network before each transformer block. This decision network operates at multiple levels:

• Patch Selection: Determines which image patches are sufficiently informative to be retained for processing in subsequent layers.
• Attention Head Selection: Identifies which attention heads in the multi-head self-attention mechanism contribute most effectively to the processing task at hand.
• Transformer Block Selection: Decides whether to skip or retain transformer blocks based on their utility for the current input.

The decision-making process is facilitated via the Gumbel-Softmax relaxation, which enables the sampling of discrete decisions in a differentiable manner, thus streamlining the training phase. The result is a model that employs computational resources in a discriminating fashion, allocating more to complex images and less to those deemed straightforward.

Implications and Future Directions

The implications of sharing computational resources according to image complexity are profound. AdaViT offers a flexible framework which could greatly benefit device-bound applications where computational resources are constrained. Additionally, the mechanism of adaptive computation resonates beyond vision transformers, suggesting potential applications in a variety of transformer-based models across different domains.

The AdaViT framework opens several paths for future research. Firstly, exploring its application to other types of transformers, such as those used in natural language processing, could yield efficiency gains in text-based models. Moreover, integration with advanced training techniques and architecture search methods could further enhance adaptive decision-making. Finally, considering broader adoption, real-world scenarios where varied image complexities are frequent, such as autonomous driving or real-time video processing, could serve as suitable applications for AdaViT's capabilities.

In summation, the paper presents AdaViT as a significant advancement towards efficient vision transformers, showcasing how adaptivity in model computation can lead to substantial improvements in processing efficiency with minimal accuracy trade-offs. The framework not only signifies an advancement in computer vision efficiency but also heralds new paradigms in how deep learning models can be optimized through adaptive strategies.