TransReID: Transformer-based Object Re-Identification (2102.04378v2)

Abstract: Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved great success, they only process one local neighborhood at a time and suffer from information loss on details caused by convolution and downsampling operators (e.g. pooling and strided convolution). To overcome these limitations, we propose a pure transformer-based object ReID framework named TransReID. Specifically, we first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods. To further enhance the robust feature learning in the context of transformers, two novel modules are carefully designed. (i) The jigsaw patch module (JPM) is proposed to rearrange the patch embeddings via shift and patch shuffle operations which generates robust features with improved discrimination ability and more diversified coverage. (ii) The side information embeddings (SIE) is introduced to mitigate feature bias towards camera/view variations by plugging in learnable embeddings to incorporate these non-visual clues. To the best of our knowledge, this is the first work to adopt a pure transformer for ReID research. Experimental results of TransReID are superior promising, which achieve state-of-the-art performance on both person and vehicle ReID benchmarks.

• The paper introduces a transformer-based ReID framework that leverages a jigsaw patch module and side information embeddings to overcome CNN limitations.
• It achieves state-of-the-art performance on benchmarks, including 67.4% mAP on MSMT17, by capturing long-range dependencies with enhanced feature discrimination.
• The method integrates non-visual cues such as camera IDs and viewing angles to boost robustness against environmental variations and viewpoint biases.

Overview of TransReID: Transformer-based Object Re-Identification

This paper presents a novel approach to object re-identification (ReID) utilizing pure transformer models, termed TransReID. Unlike conventional CNN-based methods which have been predominant in object ReID tasks, TransReID leverages the capabilities of transformers to address limitations inherent in CNN architectures, such as confining receptive fields to local regions and loss of detailed information due to downsampling operations.

Key Contributions

TransReID introduces a robust transformer-based framework for ReID, integrating two novel components: the jigsaw patch module (JPM) and side information embeddings (SIE). These advancements aim to enhance the extraction of discriminative and robust features crucial for the accurate identification of objects across varying views and environments.

1. Jigsaw Patch Module (JPM): JPM rearranges input patches through shift and shuffle operations, enabling transformers to capture long-range dependencies effectively. This approach enhances feature diversity and discrimination by encouraging the model to focus on global context rather than isolated local parts.
2. Side Information Embeddings (SIE): SIE incorporates non-visual cues such as camera IDs and viewing angles to mitigate biases associated with different environmental conditions. By embedding these factors, the framework improves robustness against viewpoint variations.

Experimental Evaluation

The proposed TransReID framework demonstrates state-of-the-art performance across multiple ReID benchmarks, including MSMT17, Market-1501, DukeMTMC-reID, and vehicle ReID datasets like VeRi-776 and VehicleID. Experimental results indicate substantial performance gains, notably achieving 67.4% mAP on MSMT17 using ViT-B/16 with overlapping patches.

Notably, TransReID outperforms several advanced CNN-based models without relying on external data like semantic parsing or pose estimation, illustrating the efficacy of transformers in capturing comprehensive and perturbation-resistant features.

Implications and Future Directions

The introduction of TransReID marks a significant shift towards using transformer architectures for ReID tasks. This shift opens up numerous possibilities for future research, particularly in:

• Model Scaling: Exploring larger transformer models or more sophisticated architectures could further enhance performance, especially if combined with techniques like model distillation.
• Real-Time Applications: While TransReID shows strong accuracy improvements, its computational requirements necessitate further exploration into efficient transformer models suitable for real-time deployment.
• Adversarial Robustness: Research could be directed towards understanding the robustness of transformer-based ReID systems against adversarial attacks, an increasingly critical concern in deployment scenarios.

In conclusion, TransReID provides a compelling case for the adoption of transformer models in object ReID, showcasing significant advancements over traditional CNN methodologies in terms of both feature representation and overall system robustness. This work paves the way for integrating transformer-based approaches in various computer vision tasks, promising substantial improvements in accuracy and reliability.