  In this paper we propose an extension of the Attention Branch Network (ABN)
by using instance segmentation for generating sharper attention maps for action
recognition. Methods for visual explanation such as Grad-CAM usually generate
blurry maps which are not intuitive for humans to understand, particularly in
recognizing actions of people in videos. Our proposed method, Object-ABN,
tackles this issue by introducing a new mask loss that makes the generated
attention maps close to the instance segmentation result. Further the PC loss
and multiple attention maps are introduced to enhance the sharpness of the maps
and improve the performance of classification. Experimental results with UCF101
and SSv2 shows that the generated maps by the proposed method are much clearer
qualitatively and quantitatively than those of the original ABN.
