4 citations · 7 across the 9 of their papers we have counts for
9 papers
Improved Measurements of and
BESIII Collaboration, M. Ablikim, M. N. Achasov +677
Using 20.3 fb of collision data collected at the center-of-mass energy of 3.773 GeV with the BESIII detector, we measure the branching fractions of …
GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation
Teli Ma, Jia Zheng, Zifan Wang +3
Learning manipulation skills from human demonstration videos offers a promising path toward generalizable and interpretable robotic intelligence-particularly through the lens of ac…
Omni-Perception: Omnidirectional Collision Avoidance for Legged Locomotion in Dynamic Environments
Zifan Wang, Teli Ma, Yufei Jia +5
Agile locomotion in complex 3D environments requires robust spatial awareness to safely avoid diverse obstacles such as aerial clutter, uneven terrain, and dynamic agents. Depth-ba…
GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping
Teli Ma, Zifan Wang, Jiaming Zhou +2
Inferring affordable (i.e., graspable) parts of arbitrary objects based on human specifications is essential for robots advancing toward open-vocabulary manipulation. Current grasp…
ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition
Jiaming Zhou, Junwei Liang, Kun-Yu Lin +2
Zero-shot action recognition (ZSAR) aims to learn an alignment model between videos and class descriptions of seen actions that is transferable to unseen actions. The text queries…
GeoDeformer: Geometric Deformable Transformer for Action Recognition
Jinhui Ye, Jiaming Zhou, Hui Xiong +1
Vision transformers have recently emerged as an effective alternative to convolutional networks for action recognition. However, vision transformers still struggle with geometric v…
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition
Jiaming Zhou, Hanjun Li, Kun-Yu Lin +1
Developing end-to-end action recognition models on long videos is fundamental and crucial for long-video action understanding. Due to the unaffordable cost of end-to-end training o…
Diversifying Spatial-Temporal Perception for Video Domain Generalization
Kun-Yu Lin, Jia-Run Du, Yipeng Gao +2
Video domain generalization aims to learn generalizable video classification models for unseen target domains by training in a source domain. A critical challenge of video domain g…
PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting
Yujin Tang, Jiaming Zhou, Xiang Pan +2
Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven…