  Temporal Action Detection (TAD) is an essential and challenging topic in
video understanding, aiming to localize the temporal segments containing human
action instances and predict the action categories. The previous works greatly
rely upon dense candidates either by designing varying anchors or enumerating
all the combinations of boundaries on video sequences; therefore, they are
related to complicated pipelines and sensitive hand-crafted designs. Recently,
with the resurgence of Transformer, query-based methods have tended to become
the rising solutions for their simplicity and flexibility. However, there still
exists a performance gap between query-based methods and well-established
methods. In this paper, we identify the main challenge lies in the large
variants of action duration and the ambiguous boundaries for short action
instances; nevertheless, quadratic-computational global attention prevents
query-based methods to build multi-scale feature maps. Towards high-quality
temporal action detection, we introduce Sparse Proposals to interact with the
hierarchical features. In our method, named SP-TAD, each proposal attends to a
local segment feature in the temporal feature pyramid. The local interaction
enables utilization of high-resolution features to preserve action instances
details. Extensive experiments demonstrate the effectiveness of our method,
especially under high tIoU thresholds. E.g., we achieve the state-of-the-art
performance on THUMOS14 (45.7% on<EMAIL_ADDRESS>33.4% on<EMAIL_ADDRESS>and 53.5% on
mAP@Avg) and competitive results on ActivityNet-1.3 (32.99% on mAP@Avg). Code
will be made available at https://github.com/wjn922/SP-TAD.
