1 citations · 1 across the 3 of their papers we have counts for
5 papers
SpikeTAD: Spiking Neural Networks for End-to-End Temporal Action Detection
Min Yang, Mi Zhou, Limin Wang
Video understanding is a crucial part of computer vision, with numerous application scenarios. With the increasing popularity of mobile devices, an increasing number of efforts are…
MobileViCLIP: An Efficient Video-Text Model for Mobile Devices
Min Yang, Zihan Jia, Zhilin Dai +2
Efficient lightweight neural networks are with increasing attention due to their faster reasoning speed and easier deployment on mobile devices. However, existing video pre-trained…
Temporal2Seq: A Unified Framework for Temporal Video Understanding Tasks
Min Yang, Zichen Zhang, Qian Dang +1
With the development of video understanding, there is a proliferation of tasks for clip-level temporal video analysis, including temporal action detection (TAD), temporal action se…
Adapting Short-Term Transformers for Action Detection in Untrimmed Videos
Min Yang, Huan Gao, Ping Guo +1
Vision Transformer (ViT) has shown high potential in video recognition, owing to its flexible design, adaptable self-attention mechanisms, and the efficacy of masked pre-training.…
BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
Min Yang, Guo Chen, Yin-Dong Zheng +2
Temporal action detection (TAD) is extensively studied in the video understanding community by generally following the object detection pipeline in images. However, complex designs…