Actor Conditioned Attention Maps for Video Action Detection
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Interactions with surrounding objects and people contain important information towards understanding human actions. In order to model such interactions explicitly, we propose to generate attention maps that rank each spatio-temporal region's importance to a detected actor. We refer to these as Actor-Conditioned Attention Maps (ACAM), and these maps serve as weights to the features extracted from the whole scene. These resulting actor-conditioned features help focus the learned model on regions that are important/relevant to the conditioned actor. Another novelty of our approach is in the use of pre-trained object detectors, instead of region proposals, that generalize better to videos from different sources. Detailed experimental results on the AVA 2.1 datasets demonstrate the importance of interactions, with a performance improvement of 5 mAP with respect to state of the art published results.
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