ClusterFormer: Clustering As A Universal Visual Learner
It proposes a novel approach for computer vision researchers seeking a unified model to handle multiple tasks efficiently, though it may be incremental in combining existing paradigms.
The paper tackles the problem of developing a universal vision model for heterogeneous tasks like image classification, object detection, and segmentation by introducing CLUSTERFORMER, which integrates clustering with transformers, achieving results such as 83.41% top-1 accuracy on ImageNet-1K and 54.2% mAP on MS COCO for object detection.
This paper presents CLUSTERFORMER, a universal vision model that is based on the CLUSTERing paradigm with TransFORMER. It comprises two novel designs: 1. recurrent cross-attention clustering, which reformulates the cross-attention mechanism in Transformer and enables recursive updates of cluster centers to facilitate strong representation learning; and 2. feature dispatching, which uses the updated cluster centers to redistribute image features through similarity-based metrics, resulting in a transparent pipeline. This elegant design streamlines an explainable and transferable workflow, capable of tackling heterogeneous vision tasks (i.e., image classification, object detection, and image segmentation) with varying levels of clustering granularity (i.e., image-, box-, and pixel-level). Empirical results demonstrate that CLUSTERFORMER outperforms various well-known specialized architectures, achieving 83.41% top-1 acc. over ImageNet-1K for image classification, 54.2% and 47.0% mAP over MS COCO for object detection and instance segmentation, 52.4% mIoU over ADE20K for semantic segmentation, and 55.8% PQ over COCO Panoptic for panoptic segmentation. For its efficacy, we hope our work can catalyze a paradigm shift in universal models in computer vision.