TCFormer: Visual Recognition via Token Clustering Transformer (2407.11321v1)

Abstract: Transformers are widely used in computer vision areas and have achieved remarkable success. Most state-of-the-art approaches split images into regular grids and represent each grid region with a vision token. However, fixed token distribution disregards the semantic meaning of different image regions, resulting in sub-optimal performance. To address this issue, we propose the Token Clustering Transformer (TCFormer), which generates dynamic vision tokens based on semantic meaning. Our dynamic tokens possess two crucial characteristics: (1) Representing image regions with similar semantic meanings using the same vision token, even if those regions are not adjacent, and (2) concentrating on regions with valuable details and represent them using fine tokens. Through extensive experimentation across various applications, including image classification, human pose estimation, semantic segmentation, and object detection, we demonstrate the effectiveness of our TCFormer. The code and models for this work are available at https://github.com/zengwang430521/TCFormer.

Overview of TCFormer: Visual Recognition via Token Clustering Transformer

The paper introduces a novel vision transformer architecture named Token Clustering Transformer (TCFormer), aimed at enhancing the capabilities of transformers in various computer vision tasks. While traditional vision transformers segment images into uniform grid regions to create vision tokens, TCFormer advances this concept by generating dynamic tokens based on semantic image features, rejecting the constraints imposed by a fixed grid structure. These dynamic tokens are fashioned to be more representative and attentive to regions with significant details, which are often overlooked when grid-based tokens are employed.

Key Contributions

TCFormer is composed of the following pivotal components, making it distinct when compared to other contemporary methods:

1. Dynamic Vision Tokens: Unlike conventional static grid tokens, dynamic tokens in TCFormer are adept at capturing semantic meanings. They can map non-adjacent regions with similar semantics into a singular token, and adjust their representational granularity depending on the region's importance in diverse tasks like image classification and human pose estimation.
2. Clustering-based Token Merge (CTM) Module: This module is integral to generating dynamic tokens. It employs a modified density peaks clustering algorithm, which clusters the feature tokens based on their semantic content rather than spatial proximity and merges them to reduce complexity while maintaining rich information.
3. Multi-stage Token Aggregation (MTA) Module: This module effectively aggregates multi-scale token features without transforming them back into a uniform feature map, maintaining the perception of details over varying resolutions. The extended variant known as CR-MTA improves reliance on token clusters, enhancing feature relations.
4. Adaptability Across Tasks: TCFormer is adaptable across a broad spectrum of vision tasks, demonstrating superior performance in image classification, semantic segmentation, object detection, and human pose estimation. The approach evidences significant gains, especially in tasks requiring detailed understanding of specific image regions such as pose estimation, where fine details are crucial.
5. Efficiency Improvements: The novel Local CTM processes and CR-MTA modules in TCFormerV2 demonstrated reduced computational burden and improved performance by incorporating token clustering and multi-scale feature aggregation processes.

Implications and Future Directions

TCFormer represents a significant step forward in vision transformer architectures by introducing flexibility and adaptability in feature representation. Future research directions include:

• Extension to More Complex Scenarios: Given its efficacy across standard vision tasks, further research could expand TCFormer’s applicability to complex, real-time tasks and video analysis, which require dynamic handling of sequence information and temporal coherence.
• Hardware Optimizations: As the dynamic tokens deviate from traditional grid-based processing, developing hardware accelerations or software frameworks to optimize the processing efficiency can alleviate time-consuming transformations.
• Integration with State-of-the-art Transformer Modules: Merging TCFormer with advanced transformer designs might lead to heightened accuracy and efficiency, suggesting possible exploration into hybrid architectures.

In conclusion, TCFormer offers a new paradigm in vision transformers through its flexible token clustering mechanism, establishing a foundation for detailed and computationally efficient image analysis. Its demonstrated performance across multiple challenging tasks points towards its potential as a general-purpose vision model, applicable to various industry and research domains.