  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.
