Vision Transformer with Progressive Sampling (2108.01684v1)

Abstract: Transformers with powerful global relation modeling abilities have been introduced to fundamental computer vision tasks recently. As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens. However, such naive tokenization could destruct object structures, assign grids to uninterested regions such as background, and introduce interference signals. To mitigate the above issues, in this paper, we propose an iterative and progressive sampling strategy to locate discriminative regions. At each iteration, embeddings of the current sampling step are fed into a transformer encoder layer, and a group of sampling offsets is predicted to update the sampling locations for the next step. The progressive sampling is differentiable. When combined with the Vision Transformer, the obtained PS-ViT network can adaptively learn where to look. The proposed PS-ViT is both effective and efficient. When trained from scratch on ImageNet, PS-ViT performs 3.8% higher than the vanilla ViT in terms of top-1 accuracy with about $4\times$ fewer parameters and $10\times$ fewer FLOPs. Code is available at https://github.com/yuexy/PS-ViT.

Vision Transformer with Progressive Sampling: Enhancements in Image Classification

The paper "Vision Transformer with Progressive Sampling" presents a significant advancement in the development of Vision Transformers (ViTs) by proposing a novel method that addresses inherent limitations in naive tokenization schemes. This approach, termed Progressive Sampling Vision Transformer (PS-ViT), leverages an iterative sampling strategy to adaptively focus on discriminative regions within an image, which leads to substantial improvements in both accuracy and efficiency.

ViTs have already established themselves as powerful tools in computer vision due to their ability to model global relations, a capability previously demonstrated in natural language processing tasks. However, a key challenge in applying transformers to vision tasks arises from their computational complexity when handling large sequences, as images contain a massive number of pixels. The conventional ViT approach, which involves partitioning images into fixed-size patches treated as tokens, often introduces inefficiencies by disrupting semantic object structures and allocating resources to uninformative background regions.

To overcome these challenges, PS-ViT introduces a progressive sampling technique that iteratively refines the locations of sampled tokens, thereby converging on regions of interest with higher semantic relevance. At each iteration, PS-ViT predicts sampling offsets that adjust the locations for the next round of sampling, guided by transformer-based encodings. This method ensures that attention is more selectively aligned with pertinent image regions, akin to the human visual system's focus mechanism.

Quantitatively, PS-ViT demonstrates its efficacy by achieving a 3.8% improvement in top-1 accuracy on ImageNet compared to the standard ViT baseline, while drastically reducing the number of parameters and floating-point operations (FLOPs) by factors of 4 and 10, respectively. Such gains indicate the model's ability to efficiently process visual data without the drawbacks of excessive computational overhead.

The implications of this research are manifold. Practically, PS-ViT offers a more scalable and computationally feasible approach for deploying transformer models in resource-constrained environments, such as edge devices and mobile applications. Theoretically, the work presents an innovative methodology for incorporating adaptive sampling into transformers, which could be extended to other modalities or tasks beyond image classification, such as object detection and segmentation.

Further developments in AI could explore the integration of progressive sampling with other architectural enhancements or learning paradigms, potentially advancing the state-of-the-art across various domains. Additionally, while this paper focuses on enhancing ViTs for image classification, there remains potential for exploring the utility of progressive sampling in video analysis tasks, considering the temporal aspects of sequential data in conjunction with spatial sampling strategies.

In conclusion, the introduction of a Vision Transformer framework that employs progressive sampling presents a considerable improvement in aligning the computational burden with the complexity of visual information. The results presented not only advance the efficacy of vision transformers but also pave the way for future innovations in intelligent visual processing systems.