10 citations · 31 across the 8 of their papers we have counts for
4 papers · 1 filter
Object-ABN: Learning to Generate Sharp Attention Maps for Action Recognition
Tomoya Nitta, Tsubasa Hirakawa, Hironobu Fujiyoshi +1
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…
Performance Evaluation of Action Recognition Models on Low Quality Videos
Aoi Otani, Ryota Hashiguchi, Kazuki Omi +2
In the design of action recognition models, the quality of videos is an important issue; however, the trade-off between the quality and performance is often ignored. In general, ac…
Model-agnostic Multi-Domain Learning with Domain-Specific Adapters for Action Recognition
Kazuki Omi, Jun Kimata, Toru Tamaki
In this paper, we propose a multi-domain learning model for action recognition. The proposed method inserts domain-specific adapters between layers of domain-independent layers of…
Vision Transformer with Cross-attention by Temporal Shift for Efficient Action Recognition
Ryota Hashiguchi, Toru Tamaki
Feature shifts have been shown to be useful for action recognition with CNN-based models since Temporal Shift Module (TSM) was proposed. It is based on frame-wise feature extractio…