DPT: Deformable Patch-based Transformer for Visual Recognition (2107.14467v1)

Abstract: Transformer has achieved great success in computer vision, while how to split patches in an image remains a problem. Existing methods usually use a fixed-size patch embedding which might destroy the semantics of objects. To address this problem, we propose a new Deformable Patch (DePatch) module which learns to adaptively split the images into patches with different positions and scales in a data-driven way rather than using predefined fixed patches. In this way, our method can well preserve the semantics in patches. The DePatch module can work as a plug-and-play module, which can easily be incorporated into different transformers to achieve an end-to-end training. We term this DePatch-embedded transformer as Deformable Patch-based Transformer (DPT) and conduct extensive evaluations of DPT on image classification and object detection. Results show DPT can achieve 81.9% top-1 accuracy on ImageNet classification, and 43.7% box mAP with RetinaNet, 44.3% with Mask R-CNN on MSCOCO object detection. Code has been made available at: https://github.com/CASIA-IVA-Lab/DPT .

• The paper introduces the DePatch module that dynamically adjusts patch size and position to preserve semantic integrity.
• It achieves a 2.3% increase in top-1 accuracy on ImageNet and significantly improves object detection metrics.
• The adaptive patch embedding approach enhances feature representation, offering robust improvements for vision transformers.

Deformable Patch-based Transformer for Visual Recognition

The paper "DPT: Deformable Patch-based Transformer for Visual Recognition" introduces an innovative approach to addressing the inherent limitations of fixed patch embedding in vision transformers. By leveraging a Deformable Patch (DePatch) module, the authors propose a solution that dynamically adjusts the size and position of patches, thereby preserving semantic integrity and enhancing feature representation in vision-based tasks.

Core Contributions and Methodology

One of the significant contributions of this paper is the introduction of the DePatch module, which autonomously learns to divide images into variably-sized patches instead of adhering to a rigid grid pattern. The module predicts offsets and scales for each patch, adapting it to the image content, thus overcoming the semantic disruption associated with fixed-size patch splits.

The process involves the following steps:

• Offset and Scale Prediction: Using a lightweight feature extractor, offsets and scales are predicted for each patch. This data-driven approach allows the model to dynamically adjust to the nuances present in diverse images.
• Adaptive Patch Embedding: By employing bilinear interpolation, the method ensures the accurate representation of patch features, adjusting these representations based on the learned offsets and scales.

The DePatch module's adaptability makes it compatible with various transformer architectures. In this paper, the authors integrate it into the Pyramid Vision Transformer (PVT), creating what they term the Deformable Patch-based Transformer (DPT).

Experimental Evaluation

The DPT's performance is rigorously assessed through experiments on both image classification and object detection benchmarks. Notably, the DPT-Tiny model achieves a top-1 accuracy of 77.4% on ImageNet classification, marking a 2.3% improvement over the PVT-Tiny baseline. Furthermore, the DPT enhances object detection metrics, notably achieving a box mAP of 43.7% with RetinaNet on the MSCOCO dataset, outperforming its PVT predecessor by a substantial margin.

Theoretical and Practical Implications

The paper's findings underscore the practical benefits of utilizing an adaptive patch embedding methodology in transformers for vision tasks. By maintaining semantic consistency across patches, the DePatch module enables the model to capture more informative representations, which is particularly beneficial for tasks requiring high precision in local feature extraction, such as object detection.

Theoretically, this research elucidates the potential of flexible, data-driven architecture designs within transformer models. The ability to embed patches that respect the intrinsic structure of visual data signifies a shift towards more semantically-aware machine learning models in computer vision.

Prospects for Future Research

The introduction of the Deformable Patch-based Transformer opens avenues for several future research directions:

• Broader Applications: While the paper primarily targets image classification and object detection, exploring the integration of DePatch into architectures for other tasks, such as semantic segmentation, is a promising area.
• Advanced Patch Embedding Methods: Leveraging advanced machine learning techniques to further refine patch division, possibly incorporating adaptive attention mechanisms, could enhance performance.
• Efficiency Optimization: Investigating ways to reduce computational overhead while maintaining the adaptability of DePatch could facilitate the deployment of such models in resource-constrained environments.

In conclusion, this work represents a significant advance in enhancing the adaptability and performance of vision transformers. By addressing the limitations of fixed patch embeddings, the DePatch module presents a robust framework for improving feature extraction fidelity in visual recognition tasks.