arXiv:1711.10143
공식 코드 · Papers with Code
Revisiting hand-crafted feature for action recognition: a set of improved dense trajectories
원문 초록
We propose a feature for action recognition called Trajectory-Set (TS), on top of the improved Dense Trajectory (iDT). The TS feature encodes only trajectories around densely sampled interest points, without any appearance features. Experimental results on the UCF50, UCF101, and HMDB51 action datasets demonstrate that TS is comparable to state-of-the-arts, and outperforms many other methods; for HMDB the accuracy of 85.4%, compared to the best accuracy of 80.2% obtained by a deep method. Our code is available on-line at https://github.com/Gauffret/TrajectorySet .
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
Talk is Not Always Cheap: Promoting Wireless Sensing Models with Text Prompts
XRF V2: A Dataset for Action Summarization with Wi-Fi Signals, and IMUs in Phones, Watches, Earbuds, and Glasses
Visual WetlandBirds Dataset: Bird Species Identification and Behavior Recognition in Videos
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
Efficient Transfer Learning for Video-language Foundation Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.