BEAR: A Video Dataset For Fine-grained Behaviors Recognition Oriented with Action and Environment Factors

Abstract: Behavior recognition is an important task in video representation learning. An essential aspect pertains to effective feature learning conducive to behavior recognition. Recently, researchers have started to study fine-grained behavior recognition, which provides similar behaviors and encourages the model to concern with more details of behaviors with effective features for distinction. However, previous fine-grained behaviors limited themselves to controlling partial information to be similar, leading to an unfair and not comprehensive evaluation of existing works. In this work, we develop a new video fine-grained behavior dataset, named BEAR, which provides fine-grained (i.e. similar) behaviors that uniquely focus on two primary factors defining behavior: Environment and Action. It includes two fine-grained behavior protocols including Fine-grained Behavior with Similar Environments and Fine-grained Behavior with Similar Actions as well as multiple sub-protocols as different scenarios. Furthermore, with this new dataset, we conduct multiple experiments with different behavior recognition models. Our research primarily explores the impact of input modality, a critical element in studying the environmental and action-based aspects of behavior recognition. Our experimental results yield intriguing insights that have substantial implications for further research endeavors.