30 citations · 43 across the 9 of their papers we have counts for
14 papers
EMPIRE: Explicit Manipulation Planning as a Learnable Intermediate Representation for Egocentric Hand-Motion Forecasting
Wen Wang, Ruibing Hou, Hong Chang +2
Forecasting dexterous hand motions from egocentric observations is fundamental to intelligent interactive systems. Existing VLM-based methods typically map observations directly to…
Revisiting Logit Distributions for Reliable Out-of-Distribution Detection
Jiachen Liang, Ruibing Hou, Minyang Hu +3
Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning models in open-world applications. While post-hoc methods are favored for their effici…
KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge
Zaifei Yang, Hong Chang, Ruibing Hou +2
The molecular large language models have garnered widespread attention due to their promising potential on molecular applications. However, current molecular large language models…
HumanPCR: Probing MLLM Capabilities in Diverse Human-Centric Scenes
Keliang Li, Hongze Shen, Hao Shi +9
The aspiration for artificial general intelligence, fueled by the rapid progress of multimodal models, demands human-comparable performance across diverse environments. We propose…
ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
Mengdi Liu, Xiaoxue Cheng, Zhangyang Gao +4
Designing protein sequences that fold into a target 3D structure, known as protein inverse folding, is a fundamental challenge in protein engineering. While recent deep learning me…
unCLIP: Improving CLIP's Visual Detail Capturing Ability via Inverting unCLIP
Yinqi Li, Jiahe Zhao, Hong Chang +3
Contrastive Language-Image Pre-training (CLIP) has become a foundation model and has been applied to various vision and multimodal tasks. However, recent works indicate that CLIP f…
UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models
Jiachen Liang, Ruibing Hou, Minyang Hu +3
Pre-trained vision-language models (e.g., CLIP) have shown powerful zero-shot transfer capabilities. But they still struggle with domain shifts and typically require labeled data t…
UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing
Yiheng Li, Ruibing Hou, Hong Chang +2
Human pose plays a crucial role in the digital age. While recent works have achieved impressive progress in understanding and generating human poses, they often support only a sing…
HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding
Keliang Li, Zaifei Yang, Jiahe Zhao +5
The significant advancements in visual understanding and instruction following from Multimodal Large Language Models (MLLMs) have opened up more possibilities for broader applicati…
Clothes-Changing Person Re-Identification with Feasibility-Aware Intermediary Matching
Jiahe Zhao, Ruibing Hou, Hong Chang +4
Current clothes-changing person re-identification (re-id) approaches usually perform retrieval based on clothes-irrelevant features, while neglecting the potential of clothes-relev…
Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications
Minyang Hu, Hong Chang, Zong Guo +3
Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by leveraging experience from \emph{related} training tasks. In this paper, we try to understand FSL…
Progressive Conservative Adaptation for Evolving Target Domains
Gangming Zhao, Chaoqi Chen, Wenhao He +5
Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge seque…
BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models
Shaolei Zhang, Qingkai Fang, Zhuocheng Zhang +8
Large language models (LLMs) have demonstrated remarkable prowess in language understanding and generation. Advancing from foundation LLMs to instructionfollowing LLMs, instruction…
Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework
Botao Ye, Hong Chang, Bingpeng Ma +2
The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted…