7 papers
ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection
Tongtong Wang, Mingzhu Xu, Chenglong Yu +3
InfRared Small Target Detection (IRSTD) is a challenging task. Relying solely on pixel-level information, vision-only methods struggle to distinguish targets from clutter. Current…
HUD: Hierarchical Uncertainty-Aware Disambiguation Network for Composed Video Retrieval
Zhiwei Chen, Yupeng Hu, Zixu Li +3
Composed Video Retrieval (CVR) is a challenging video retrieval task that utilizes multi-modal queries, consisting of a reference video and modification text, to retrieve the desir…
From Mannequin to Human: A Pose-Aware and Identity-Preserving Video Generation Framework for Lifelike Clothing Display
Xiangyu Mu, Dongliang Zhou, Jie Hou +2
Mannequin-based clothing displays offer a cost-effective alternative to real-model showcases for online fashion presentation, but lack realism and expressive detail. To overcome th…
PVLM: Parsing-Aware Vision Language Model with Dynamic Contrastive Learning for Zero-Shot Deepfake Attribution
Yaning Zhang, Jiahe Zhang, Chunjie Ma +3
The challenge of tracing the source attribution of forged faces has gained significant attention due to the rapid advancement of generative models. However, existing deepfake attri…
Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification
Shengxun Wei, Zan Gao, Chunjie Ma +3
Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. Th…
Social Debiasing for Fair Multi-modal LLMs
Harry Cheng, Yangyang Guo, Qingpei Guo +4
Multi-modal Large Language Models (MLLMs) have dramatically advanced the research field and delivered powerful vision-language understanding capabilities. However, these models oft…
Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory
Wenliang Zhong, Haoyu Tang, Qinghai Zheng +3
The rapid evolution of deep learning and large language models has led to an exponential growth in the demand for training data, prompting the development of Dataset Distillation m…