Multiscale Vision Transformers meet Bipartite Matching for efficient single-stage Action Localization
This addresses the challenge of real-time deployment for action localization by offering a more efficient single-stage solution, though it appears incremental as it builds on existing vision transformer and bipartite matching techniques.
The paper tackles the problem of action localization by proposing a single-stage method that applies a bipartite matching loss to vision transformer output tokens, eliminating the need for an extra encoder-decoder head and learnable queries, achieving a +3 mAP improvement on AVA2.2 compared to a two-stage counterpart.
Action Localization is a challenging problem that combines detection and recognition tasks, which are often addressed separately. State-of-the-art methods rely on off-the-shelf bounding box detections pre-computed at high resolution, and propose transformer models that focus on the classification task alone. Such two-stage solutions are prohibitive for real-time deployment. On the other hand, single-stage methods target both tasks by devoting part of the network (generally the backbone) to sharing the majority of the workload, compromising performance for speed. These methods build on adding a DETR head with learnable queries that after cross- and self-attention can be sent to corresponding MLPs for detecting a person's bounding box and action. However, DETR-like architectures are challenging to train and can incur in big complexity. In this paper, we observe that \textbf{a straight bipartite matching loss can be applied to the output tokens of a vision transformer}. This results in a backbone + MLP architecture that can do both tasks without the need of an extra encoder-decoder head and learnable queries. We show that a single MViTv2-S architecture trained with bipartite matching to perform both tasks surpasses the same MViTv2-S when trained with RoI align on pre-computed bounding boxes. With a careful design of token pooling and the proposed training pipeline, our Bipartite-Matching Vision Transformer model, \textbf{BMViT}, achieves +3 mAP on AVA2.2. w.r.t. the two-stage MViTv2-S counterpart. Code is available at \href{https://github.com/IoannaNti/BMViT}{https://github.com/IoannaNti/BMViT}