Self-Feedback DETR for Temporal Action Detection
Jihwan Kim Miso Lee Jae-Pil Heo
Temporal Action Detection (TAD) is challenging but fundamental for real-world
video applications. Recently, DETR-based models have been devised for TAD but
have not performed well yet. In this paper, we point out the problem in the
self-attention of DETR for TAD; the attention modules focus on a few key
elements, called temporal collapse problem. It degrades the capability of the
encoder and decoder since their self-attention modules play no role. To solve
the problem, we propose a novel framework, Self-DETR, which utilizes
cross-attention maps of the decoder to reactivate self-attention modules. We
recover the relationship between encoder features by simple matrix
multiplication of the cross-attention map and its transpose. Likewise, we also
get the information within decoder queries. By guiding collapsed self-attention
maps with the guidance map calculated, we settle down the temporal collapse of
self-attention modules in the encoder and decoder. Our extensive experiments
demonstrate that Self-DETR resolves the temporal collapse problem by keeping
high diversity of attention over all layers.