2 citations · 3 across the 12 of their papers we have counts for
14 papers
TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation
Mingwei Li, Yi Yang, Hehe Fan
Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studied error source: VAE reconstruction degradation. The…
Convex-Hull-Neighborhood Smooth Dual Generalization: Controlling Local Correction Propagation in Offline RL
Yi Yang, Zhennan Chen, Mingfeng Lv +3
Offline reinforcement learning (offline RL) can benefit from nearby out-of-distribution (OOD) actions, but estimation errors at these actions may be amplified by bootstrapping. Exi…
One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement
Yixiao Zhou, Dongzhou Cheng, zhiliang wu +3
Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquiries and the structured logic r…
OmniVTG: A Large-Scale Dataset and Training Paradigm for Open-World Video Temporal Grounding
Minghang Zheng, Zihao Yin, Yi Yang +2
Video Temporal Grounding (VTG), the task of localizing video segments from text queries, struggles in open-world settings due to limited dataset scale and semantic diversity, causi…
Let's Reward Step-by-Step: Step-Aware Contrastive Alignment for Vision-Language Navigation in Continuous Environments
Haoyuan Li, Rui Liu, Hehe Fan +1
Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to learn complex reasoning from long-horizon human interactions. While Multi-modal Large Language Mod…
TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation
Mingwei Li, Hehe Fan, Yi Yang
Monocular normal estimation for transparent objects is critical for laboratory automation, yet it remains challenging due to complex light refraction and reflection. These optical…
DeMoGen: Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models
Jianrong Zhang, Hehe Fan, Yi Yang
Human motions are compositional: complex behaviors can be described as combinations of simpler primitives. However, existing approaches primarily focus on forward modeling, e.g., l…
Endowing GPT-4 with a Humanoid Body: Building the Bridge Between Off-the-Shelf VLMs and the Physical World
Yingzhao Jian, Zhongan Wang, Yi Yang +1
Humanoid agents often struggle to handle flexible and diverse interactions in open environments. A common solution is to collect massive datasets to train a highly capable model, b…
Translution: Unifying Self-attention and Convolution for Adaptive and Relative Modeling
Hehe Fan, Yi Yang, Mohan Kankanhalli +1
When modeling a given type of data, we consider it to involve two key aspects: 1) identifying relevant elements (e.g., image pixels or textual words) to a central element, as in a…
GraphTARIF: Linear Graph Transformer with Augmented Rank and Improved Focus
Zhaolin Hu, Kun Li, Hehe Fan +1
Linear attention mechanisms have emerged as efficient alternatives to full self-attention in Graph Transformers, offering linear time complexity. However, existing linear attention…
TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priors
Mingwei Li, Pu Pang, Hehe Fan +2
Reconstructing transparent surfaces is essential for tasks such as robotic manipulation in labs, yet it poses a significant challenge for 3D reconstruction techniques like 3D Gauss…
EnergyMoGen: Compositional Human Motion Generation with Energy-Based Diffusion Model in Latent Space
Jianrong Zhang, Hehe Fan, Yi Yang
Diffusion models, particularly latent diffusion models, have demonstrated remarkable success in text-driven human motion generation. However, it remains challenging for latent diff…
Prompt-Aware Adapter: Towards Learning Adaptive Visual Tokens for Multimodal Large Language Models
Yue Zhang, Hehe Fan, Yi Yang
To bridge the gap between vision and language modalities, Multimodal Large Language Models (MLLMs) usually learn an adapter that converts visual inputs to understandable tokens for…
TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-Alignment
Wei Li, Hehe Fan, Yongkang Wong +2
Recent advancements in image understanding have benefited from the extensive use of web image-text pairs. However, video understanding remains a challenge despite the availability…