29 citations · 77 across the 17 of their papers we have counts for
5 papers · 1 filter
Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning
Yuxiang Lu, Shengcao Cao, Yu-Xiong Wang
Vision Foundation Models (VFMs) have demonstrated outstanding performance on numerous downstream tasks. However, due to their inherent representation biases originating from differ…
Emergent Visual Grounding in Large Multimodal Models Without Grounding Supervision
Shengcao Cao, Liang-Yan Gui, Yu-Xiong Wang
Current large multimodal models (LMMs) face challenges in grounding, which requires the model to relate language components to visual entities. Contrary to the common practice that…
TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding
Zhihao Zhang, Shengcao Cao, Yu-Xiong Wang
The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge fr…
SOHES: Self-supervised Open-world Hierarchical Entity Segmentation
Shengcao Cao, Jiuxiang Gu, Jason Kuen +7
Open-world entity segmentation, as an emerging computer vision task, aims at segmenting entities in images without being restricted by pre-defined classes, offering impressive gene…
HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui +1
The human visual perception system demonstrates exceptional capabilities in learning without explicit supervision and understanding the part-to-whole composition of objects. Drawin…