46 citations · 49 across the 9 of their papers we have counts for
10 papers
Mono3DVG-EnSD: Enhanced Spatial-aware and Dimension-decoupled Text Encoding for Monocular 3D Visual Grounding
Yuzhen Li, Min Liu, Zhaoyang Li +4
Monocular 3D Visual Grounding (Mono3DVG) is an emerging task that locates 3D objects in RGB images using text descriptions with geometric cues. However, existing methods face two k…
A Dual-stage Prompt-driven Privacy-preserving Paradigm for Person Re-Identification
Ruolin Li, Min Liu, Yuan Bian +4
With growing concerns over data privacy, researchers have started using virtual data as an alternative to sensitive real-world images for training person re-identification (Re-ID)…
MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output
Yanyuan Chen, Dexuan Xu, Yu Huang +6
Currently, medical vision language models are widely used in medical vision question answering tasks. However, existing models are confronted with two issues: for input, the model…
Dual Enhancement on 3D Vision-Language Perception for Monocular 3D Visual Grounding
Yuzhen Li, Min Liu, Yuan Bian +4
Monocular 3D visual grounding is a novel task that aims to locate 3D objects in RGB images using text descriptions with explicit geometry information. Despite the inclusion of geom…
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion
Yuan Bian, Min Liu, Yunqi Yi +2
Person re-identification (re-id) models are vital in security surveillance systems, requiring transferable adversarial attacks to explore the vulnerabilities of them. Recently, vis…
Modality Unified Attack for Omni-Modality Person Re-Identification
Yuan Bian, Min Liu, Yunqi Yi +3
Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and c…
Learning to Learn Transferable Generative Attack for Person Re-Identification
Yuan Bian, Min Liu, Xueping Wang +2
Deep learning-based person re-identification (re-id) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial att…
Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency
Xueping Wang, Shasha Li, Min Liu +2
The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to m…
Learning Person Re-identification Models from Videos with Weak Supervision
Xueping Wang, Sujoy Paul, Dripta S. Raychaudhuri +3
Most person re-identification methods, being supervised techniques, suffer from the burden of massive annotation requirement. Unsupervised methods overcome this need for labeled da…
Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification
Xueping Wang, Rameswar Panda, Min Liu +2
Many unsupervised approaches have been proposed recently for the video-based re-identification problem since annotations of samples across cameras are time-consuming. However, high…