Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer (2204.08680v3)

Abstract: Vision transformers have achieved great successes in many computer vision tasks. Most methods generate vision tokens by splitting an image into a regular and fixed grid and treating each cell as a token. However, not all regions are equally important in human-centric vision tasks, e.g., the human body needs a fine representation with many tokens, while the image background can be modeled by a few tokens. To address this problem, we propose a novel Vision Transformer, called Token Clustering Transformer (TCFormer), which merges tokens by progressive clustering, where the tokens can be merged from different locations with flexible shapes and sizes. The tokens in TCFormer can not only focus on important areas but also adjust the token shapes to fit the semantic concept and adopt a fine resolution for regions containing critical details, which is beneficial to capturing detailed information. Extensive experiments show that TCFormer consistently outperforms its counterparts on different challenging human-centric tasks and datasets, including whole-body pose estimation on COCO-WholeBody and 3D human mesh reconstruction on 3DPW. Code is available at https://github.com/zengwang430521/TCFormer.git

An Examination of Token Clustering Transformer (TCFormer) for Human-Centric Visual Analysis

The paper "Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer" introduces a novel approach to enhance vision transformers (ViTs) specifically for human-centric tasks in computer vision. These tasks include whole-body pose estimation, face alignment, and 3D human mesh reconstruction, which are crucial for applications like augmented reality and action recognition. The proposed model, Token Clustering Transformer (TCFormer), addresses limitations in traditional vision transformers by dynamically generating tokens through a clustering mechanism tailored to the semantic importance of different image regions.

Key Contributions

1. Token Clustering Transformer (TCFormer): The TCFormer is introduced as a model that detects hierarchically by progressively clustering tokens. This adapts to different image areas by assigning more tokens to significant regions, such as the human body, while reducing tokens in less critical background regions. The ability to cluster tokens dynamically in various shapes and sizes allows TCFormer to concentrate computational resources on capturing vital details.
2. Clustering-Based Token Merge (CTM) Block: A novel CTM block is developed to merge tokens with similar semantic meanings. It employs a K-nearest neighbors-based density peaks clustering algorithm. This process helps in maintaining the semantic integrity of image features during clustering, which is shown to be crucial for extracting details in complex tasks like pose estimation.
3. Multi-Stage Token Aggregation (MTA) Head: The MTA head facilitates feature aggregation across multiple stages of the transformer, preserving detailed information throughout the network. This is particularly important as it mitigates information loss that typically occurs when high-resolution features are directly transformed into grid structures.

Experimental Evaluation

The experimental results indicate the superior performance of TCFormer across various datasets compared to traditional models. For COCO-WholeBody, TCFormer achieved a substantial gain in Average Precision (AP) and Average Recall (AR) compared to state-of-the-art models. The model's effectiveness in allocating tokens effectively is particularly noticeable in tasks focusing on small but detailed regions like hand and face keypoints.

In 3D human mesh reconstruction, TCFormer exhibited competitive performance, outperforming numerous baseline models on both 3DPW and Human3.6M datasets. Furthermore, its application to face alignment on the WFLW dataset demonstrated its robustness to challenges such as occlusion and adverse lighting conditions, achieving lower normalized mean error (NME) than several existing methods.

Implications and Future Directions

The introduction of TCFormer signifies an important step in the ongoing advancement of vision transformers. By emphasizing semantic importance through adaptive token clustering, TCFormer could substantially influence future developments in both human-centric and general visual tasks. The adaptability and dynamic nature of this approach are promising for tasks requiring attention to specific image details, potentially leading to advancements in fine-grained image analysis.

Looking forward, potential directions for expanding upon the TCFormer include exploring object detection and semantic segmentation tasks, which could benefit from its token clustering capabilities. Additionally, while the complexity of clustering could be a limitation for high-resolution inputs, strategies like part-wise clustering could alleviate computational burdens and maintain efficiency.

In conclusion, TCFormer represents a distinctive advancement in the field of human-centric visual analysis, providing a scalable framework that enhances detail capture and overall model efficiency. This research offers a compelling contribution to the field of computer vision, pointing towards more nuanced approaches to tokenization in vision transformers.