4 citations · 5 across the 7 of their papers we have counts for
9 papers
CoCo-IR: Contextual Composed Image Retrieval
Shengcao Cao, Tanmaya Shekhar Dabral, Zhongli Ding +6
Current instruction-based image retrieval systems are powerful but limited to single-turn interactions, failing to capture the iterative nature of complex, real-world visual search…
Think in Latent, Explain in Language: Self-Explainable Latent Reasoning
Dayuan Zhao, Shengcao Cao, Yu-Xiong Wang +1
Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by compressing verbose reasoning…
DCNNAnaCal: Physics-Informed Machine Learning for Accurate and Precise Weak Lensing Shear Estimation
Shurui Lin, Xiangchong Li, Ji Li +3
Traditional weak gravitational lensing shear estimators are carefully calibrated but struggle to fully capture realistic galaxy morphologies, point-spread-function (PSF) effects, b…
Refer to Any Segmentation Mask Group With Vision-Language Prompts
Shengcao Cao, Zijun Wei, Jason Kuen +6
Recent image segmentation models have advanced to segment images into high-quality masks for visual entities, and yet they cannot provide comprehensive semantic understanding for c…
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…