7 papers
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…