Transformer-based Action recognition in hand-object interacting scenarios
Not on the map. Why?
Abstract (arXiv)
This report describes the 2nd place solution to the ECCV 2022 Human Body, Hands, and Activities (HBHA) from Egocentric and Multi-view Cameras Challenge: Action Recognition. This challenge aims to recognize hand-object interaction in an egocentric view. We propose a framework that estimates keypoints of two hands and an object with a Transformer-based keypoint estimator and recognizes actions based on the estimated keypoints. We achieved a top-1 accuracy of 87.19% on the testset.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 17 references that are in the map. First posted on arXiv 20 Oct 2022.
Builds on
14 of 17 references.
References · 14
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
- 2017Non-local Neural Networkscited by 2,293
- 2018SlowFast Networks for Video Recognitioncited by 1,867
- 2021ViViT: A Video Vision Transformercited by 1,620
- 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
- 2021DexYCB: A Benchmark for Capturing Hand Grasping of Objectscited by 325
- 2021Revisiting Skeleton-based Action Recognitioncited by 257
- 2021H2O: Two Hands Manipulating Objects for First Person Interaction Recognitioncited by 254
- 2017First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotationscited by 230
- 2019H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactionscited by 141
- 2019Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Renderingcited by 115
2 more
Built on by
Citing papers per year: 2023: 1; 2024: 1.
2 papers.