kMaX-DeepLab: k-means Mask Transformer (2207.04044v5)

Published 8 Jul 2022 in cs.CV

Abstract: The rise of transformers in vision tasks not only advances network backbone designs, but also starts a brand-new page to achieve end-to-end image recognition (e.g., object detection and panoptic segmentation). Originated from NLP, transformer architectures, consisting of self-attention and cross-attention, effectively learn long-range interactions between elements in a sequence. However, we observe that most existing transformer-based vision models simply borrow the idea from NLP, neglecting the crucial difference between languages and images, particularly the extremely large sequence length of spatially flattened pixel features. This subsequently impedes the learning in cross-attention between pixel features and object queries. In this paper, we rethink the relationship between pixels and object queries and propose to reformulate the cross-attention learning as a clustering process. Inspired by the traditional k-means clustering algorithm, we develop a k-means Mask Xformer (kMaX-DeepLab) for segmentation tasks, which not only improves the state-of-the-art, but also enjoys a simple and elegant design. As a result, our kMaX-DeepLab achieves a new state-of-the-art performance on COCO val set with 58.0% PQ, Cityscapes val set with 68.4% PQ, 44.0% AP, and 83.5% mIoU, and ADE20K val set with 50.9% PQ and 55.2% mIoU without test-time augmentation or external dataset. We hope our work can shed some light on designing transformers tailored for vision tasks. TensorFlow code and models are available at https://github.com/google-research/deeplab2 A PyTorch re-implementation is also available at https://github.com/bytedance/kmax-deeplab

References (2)

Authors (8)

Qihang Yu (44 papers)
Huiyu Wang (38 papers)
Siyuan Qiao (40 papers)
Maxwell Collins (6 papers)
Yukun Zhu (33 papers)
Hartwig Adam (49 papers)
Alan Yuille (294 papers)
Liang-Chieh Chen (66 papers)

Citations (13)

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Summary

Analyzing kMaX-DeepLab: k-Means Mask Transformer

The paper "kMaX-DeepLab: k-Means Mask Transformer" introduces a novel transformer architecture, $\mathbf{k}$ -MaX-DeepLab, designed for segmentation tasks in computer vision. This work presents an innovative approach by reformulating the conventional cross-attention mechanism, prevalent in transformer architectures, into a clustering framework inspired by the $k$ -means algorithm. The key insight here is treating object queries as cluster centers, which allows the authors to redesign the cross-attention as a clustering process.

Methodology and Contributions

Transformers have revolutionized visual recognition tasks by adopting attention mechanisms originating from NLP. Typically, vision transformers employ self-attention and cross-attention modules that handle long-range dependencies in images. However, conventional cross-attention in vision tasks faces challenges due to the extensive sequence lengths of flattened image pixel features. The proposed $\mathbf{k}$ -means cross-attention aims to alleviate this by performing a cluster-wise argmax, which resembles the hard assignment step in the $k$ -means algorithm. This design effectively reduces the complexity of cross-attention by focusing on a smaller number of cluster centers instead of an entire spatial resolution, promoting faster convergence and improved performance.

The $\mathbf{k}$ MaX-DeepLab introduces a series of decode stages that apply this concept to iteratively refine cluster assignments, improving the mask predictions for segmentation tasks. This adjustment leads to significant improvements over traditional cross-attention mechanisms, evidenced by performance gains across several benchmark datasets.

Numerical Results and Strong Claims

The experimental results demonstrate that the $\mathbf{k}$ MaX-DeepLab model outperforms state-of-the-art methods on multiple datasets. For instance, it achieves a 58.0% PQ on the COCO validation set using a ConvNeXt-L backbone, indicating nearly 3.4% improvement over K-Net with a Swin-L backbone. Moreover, with a ResNet-50 backbone, $\mathbf{k}$ MaX-DeepLab presents a significant 5.2% PQ gain over the original cross-attention models. The efficacy of the model is further underscored by efficiency metrics, requiring fewer parameters and FLOPs compared to competing models while attaining higher accuracy.

Implications and Future Directions

The implications of this research span both theoretical and practical realms. Theoretically, it questions the necessity of certain complex attention mechanisms in vision-based transformers, proposing clustering as a viable alternative for object query-pixel feature interaction. Practically, the model’s superior performance on established datasets without external data or test-time augmentation suggests a strong candidate for real-world applications where computational resources are constrained.

Looking forward, this work may inspire further exploration into clustering-based mechanisms for various vision tasks, potentially expanding beyond segmentation to areas like object detection and feature tracking. Future research could examine the scalability of this approach to even larger datasets and if additional refinements to the clustering mechanisms could drive further efficiencies or improvements.

By aligning object queries with clustering centers, $\mathbf{k}$ MaX-DeepLab offers a refreshing perspective on vision transformers, promising a more efficient and effective approach to segmentation challenges in computer vision.

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