9 papers
Learning to See through Illumination Extremes with Event Streaming in Multimodal Large Language Models
Baoheng Zhang, Jiahui Liu, Gui Zhao +7
Multimodal Large Language Models (MLLMs) perform strong vision-language reasoning under standard conditions but fail in extreme illumination, where RGB inputs lose irrevocable stru…
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
Mingle Zhou, Jiahui Liu, Jin Wan +2
Unsupervised Continuous Anomaly Detection (UCAD) is gaining attention for effectively addressing the catastrophic forgetting and heavy computational burden issues in traditional Un…
DIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection
Weilin Zhou, Zonghao Ying, Chunlei Meng +6
Multimodal fake news detection is crucial for mitigating adversarial misinformation. Existing methods, relying on static fusion or LLMs, face computational redundancy and hallucina…
ASSIST-3D: Adapted Scene Synthesis for Class-Agnostic 3D Instance Segmentation
Shengchao Zhou, Jiehong Lin, Jiahui Liu +3
Class-agnostic 3D instance segmentation tackles the challenging task of segmenting all object instances, including previously unseen ones, without semantic class reliance. Current…
Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detection
Shizhen Zhao, Jiahui Liu, Xin Wen +2
Pre-trained vision foundation models have transformed many computer vision tasks. Despite their strong ability to learn discriminative and generalizable features crucial for out-of…
Aligning Effective Tokens with Video Anomaly in Large Language Models
Yingxian Chen, Jiahui Liu, Ruidi Fan +6
Understanding abnormal events in videos is a vital and challenging task that has garnered significant attention in a wide range of applications. Although current video understandin…
Learning from Neighbors: Category Extrapolation for Long-Tail Learning
Shizhen Zhao, Xin Wen, Jiahui Liu +3
Balancing training on long-tail data distributions remains a long-standing challenge in deep learning. While methods such as re-weighting and re-sampling help alleviate the imbalan…
Can OOD Object Detectors Learn from Foundation Models?
Jiahui Liu, Xin Wen, Shizhen Zhao +2
Out-of-distribution (OOD) object detection is a challenging task due to the absence of open-set OOD data. Inspired by recent advancements in text-to-image generative models, such a…
Can 3D Vision-Language Models Truly Understand Natural Language?
Weipeng Deng, Jihan Yang, Runyu Ding +4
Rapid advancements in 3D vision-language (3D-VL) tasks have opened up new avenues for human interaction with embodied agents or robots using natural language. Despite this progress…