Dual DETRs for Multi-Label Temporal Action Detection
Authors: Yuhan Zhu, Guozhen Zhang, Jing Tan, Gangshan Wu, Limin Wang

Temporal Action Detection (TAD) aims to identify the action boundaries and the corresponding category within untrimmed videos. Inspired by the success of DETR in object detection several methods have adapted the query-based framework to the TAD task. However these approaches primarily followed DETR to predict actions at the instance level (i.e. identify each action by its center point) leading to sub-optimal boundary localization. To address this issue we propose a new Dual-level query-based TAD framework namely DualDETR to detect actions from both instance-level and boundary-level. Decoding at different levels requires semantics of different granularity therefore we introduce a two-branch decoding structure. This structure builds distinctive decoding processes for different levels facilitating explicit capture of temporal cues and semantics at each level. On top of the two-branch design we present a joint query initialization strategy to align queries from both levels. Specifically we leverage encoder proposals to match queries from each level in a one-to-one manner. Then the matched queries are initialized using position and content prior from the matched action proposal. The aligned dual-level queries can refine the matched proposal with complementary cues during subsequent decoding. We evaluate DualDETR on three challenging multi-label TAD benchmarks. The experimental results demonstrate the superior performance of DualDETR to the existing state-of-the-art methods achieving a substantial improvement under det-mAP and delivering impressive results under seg-mAP.