2 citations · 3 across the 7 of their papers we have counts for
7 papers
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation
Xingyu Zhu, Junfeng Fang, Shuo Wang +4
Large Vision-Language Models (LVLMs) exhibit powerful generative capabilities but frequently produce hallucinations that compromise output reliability. Fine-tuning on annotated dat…
GuardAlign: Test-time Safety Alignment in Multimodal Large Language Models
Xingyu Zhu, Beier Zhu, Junfeng Fang +4
Large vision-language models (LVLMs) have achieved remarkable progress in vision-language reasoning tasks, yet ensuring their safety remains a critical challenge. Recent input-side…
SeViCES: Unifying Semantic-Visual Evidence Consensus for Long Video Understanding
Yuan Sheng, Yanbin Hao, Chenxu Li +2
Long video understanding remains challenging due to its complex, diverse, and temporally scattered content. Although video large language models (Video-LLMs) can process videos las…
Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters
Yuan Wang, Ouxiang Li, Tingting Mu +4
Recent success of text-to-image (T2I) generation and its increasing practical applications, enabled by diffusion models, require urgent consideration of erasing unwanted concepts,…
Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
Zhicai Wang, Longhui Wei, Tan Wang +5
Text-to-image (T2I) generative models have recently emerged as a powerful tool, enabling the creation of photo-realistic images and giving rise to a multitude of applications. Howe…
Boosting Few-Shot Learning via Attentive Feature Regularization
Xingyu Zhu, Shuo Wang, Jinda Lu +3
Few-shot learning (FSL) based on manifold regularization aims to improve the recognition capacity of novel objects with limited training samples by mixing two samples from differen…
Selective Volume Mixup for Video Action Recognition
Yi Tan, Zhaofan Qiu, Yanbin Hao +2
The recent advances in Convolutional Neural Networks (CNNs) and Vision Transformers have convincingly demonstrated high learning capability for video action recognition on large da…