37 citations · 41 across the 19 of their papers we have counts for
22 papers
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching
Bui Van Hung, Dang Thanh Vu, Hoang-Son Vo +3
The inherent variability of fMRI responses across trials encourages a generative view of the stimulus-to-response mapping. Prior generative encoders often operate in compressed lat…
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…
Task-disentangled Low-Rank Adaptation for Versatile Audio-visual Multi-modal Learning Tasks within a Unified Framework
Hanyu Xuan, Mengqi Zhang, Junjun Mao +5
Inspired by human multi-modal perception, Audio-Visual Multi-Modal Learning (AVMML) integrates auditory and visual information to leverage complementary cross-modal cues, enabling…
SmartMage: Dynamic Modality Orchestration for 3D Scene Understanding
Yue Zhang, Yingzhao Jian, Yunqiu Xu +2
Understanding 3D scenes is fundamental to embodied intelligence, requiring joint reasoning over heterogeneous information from multiple modalities, including visual and geometric c…
Large language model agents accelerate inverse design of metal-organic frameworks for gas separation
Zhaolin Hu, Hehe Fan, Wangyihan Guo +4
Metal-organic frameworks (MOFs) offer a highly modular platform for adsorptive gas separation, yet their vast reticular design space makes inverse design difficult under simultaneo…
Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction
Yingzhao Jian, Zihao Lin, Hehe Fan
The 3D geometry of real-world scene data is often incomplete. Mainstream methods use depth estimators to inpaint missing structure. However, their prediction results can be inconsi…
Skill-3D: Evolving Scene-Aware Skills for Agentic 3D Spatial Reasoning
Haoyuan Li, Zhengdong Hu, Jun Wang +2
This paper explores agentic 3D spatial understanding, i.e., MLLM agents performing 3D reasoning through tool use. Existing methods often misuse tools and exhibit biased tool prefer…
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…
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…
Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion models
Ruisi Zhao, Haoren Zheng, Zongxin Yang +2
Rigged 3D assets are fundamental to 3D deformation and animation. However, existing 3D generation methods face challenges in generating animatable geometry, while rigging technique…
OSCAgent: Accelerating the Discovery of Organic Solar Cells with LLM Agents
Zhaolin Hu, Zhiliang Wu, Kun Li +2
Organic solar cells (OSCs) hold great promise for sustainable energy, but discovering high-performance materials is time-consuming and costly. Existing molecular generation methods…
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…
Text-Scene: A Scene-to-Language Parsing Framework for 3D Scene Understanding
Haoyuan Li, Rui Liu, Hehe Fan +1
Enabling agents to understand and interact with complex 3D scenes is a fundamental challenge for embodied artificial intelligence systems. While Multimodal Large Language Models (M…
Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs
Yixiao Zhou, Ziyu Zhao, Dongzhou Cheng +6
Sparse Mixture-of-Experts (SMoE) architectures are widely used in large language models (LLMs) due to their computational efficiency. However, though only a few experts are activat…
Motion Matters: Motion-guided Modulation Network for Skeleton-based Micro-Action Recognition
Jihao Gu, Kun Li, Fei Wang +4
Micro-Actions (MAs) are an important form of non-verbal communication in social interactions, with potential applications in human emotional analysis. However, existing methods in…
BVINet: Unlocking Blind Video Inpainting with Zero Annotations
Zhiliang Wu, Kerui Chen, Kun Li +2
Video inpainting aims to fill in corrupted regions of the video with plausible contents. Existing methods generally assume that the locations of corrupted regions are known, focusi…
Prompt-Aware Controllable Shadow Removal
Kerui Chen, Zhiliang Wu, Wenjin Hou +3
Shadow removal aims to restore the image content in shadowed regions. While deep learning-based methods have shown promising results, they still face key challenges: 1) uncontrolle…
Prototypical Calibrating Ambiguous Samples for Micro-Action Recognition
Kun Li, Dan Guo, Guoliang Chen +5
Micro-Action Recognition (MAR) has gained increasing attention due to its crucial role as a form of non-verbal communication in social interactions, with promising potential for ap…