Actor-agnostic Multi-label Action Recognition with Multi-modal Query

• URL: http://arxiv.org/abs/2307.10763v3
• Date: Wed, 10 Jan 2024 12:18:40 GMT
• Title: Actor-agnostic Multi-label Action Recognition with Multi-modal Query
• Authors: Anindya Mondal, Sauradip Nag, Joaquin M Prada, Xiatian Zhu, Anjan Dutta
• Abstract summary: Existing action recognition methods are typically actor-specific. This requires actor-specific pose estimation (e.g., humans vs. animals) We propose a new approach called 'actor-agnostic multi-modal multi-label action recognition'
• Score: 42.38571663534819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing action recognition methods are typically actor-specific due to the intrinsic topological and apparent differences among the actors. This requires actor-specific pose estimation (e.g., humans vs. animals), leading to cumbersome model design complexity and high maintenance costs. Moreover, they often focus on learning the visual modality alone and single-label classification whilst neglecting other available information sources (e.g., class name text) and the concurrent occurrence of multiple actions. To overcome these limitations, we propose a new approach called 'actor-agnostic multi-modal multi-label action recognition,' which offers a unified solution for various types of actors, including humans and animals. We further formulate a novel Multi-modal Semantic Query Network (MSQNet) model in a transformer-based object detection framework (e.g., DETR), characterized by leveraging visual and textual modalities to represent the action classes better. The elimination of actor-specific model designs is a key advantage, as it removes the need for actor pose estimation altogether. Extensive experiments on five publicly available benchmarks show that our MSQNet consistently outperforms the prior arts of actor-specific alternatives on human and animal single- and multi-label action recognition tasks by up to 50%. Code is made available at https://github.com/mondalanindya/MSQNet.

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