10 citations · 44 across the 34 of their papers we have counts for
37 papers
VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking
Jiyuan Fu, Kaixun Jiang, Jingkai Jia +7
While Vision-Language-Action (VLA) models have emerged as powerful generalist policies, their severe vulnerability to adversarial patches significantly hinders their deployment in…
Unified Multimodal Visual Tracking with Dual Mixture-of-Experts
Lingyi Hong, Jinglun Li, Xinyu Zhou +6
Multimodal visual object tracking can be divided into to several kinds of tasks (e.g. RGB and RGB+X tracking), based on the input modality. Existing methods often train separate mo…
GenAgent: Scaling Text-to-Image Generation via Agentic Multimodal Reasoning
Kaixun Jiang, Yuzheng Wang, Junjie Zhou +6
We introduce GenAgent, unifying visual understanding and generation through an agentic multimodal model. Unlike unified models that face expensive training costs and understanding-…
Dynamic Semantic-Aware Correlation Modeling for UAV Tracking
Xinyu Zhou, Tongxin Pan, Lingyi Hong +5
UAV tracking can be widely applied in scenarios such as disaster rescue, environmental monitoring, and logistics transportation. However, existing UAV tracking methods predominantl…
Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detection
Jinglun Li, Kaixun Jiang, Zhaoyu Chen +4
Pre-trained vision-language models have exhibited remarkable abilities in detecting out-of-distribution (OOD) samples. However, some challenging OOD samples, which lie close to in-…
Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment
Kaixun Jiang, Zhaoyu Chen, Haijing Guo +6
Preference alignment in diffusion models has primarily focused on benign human preferences (e.g., aesthetic). In this paper, we propose a novel perspective: framing unrestricted ad…
LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
Jiyuan Fu, Kaixun Jiang, Lingyi Hong +5
Multimodal Large Language Models (MLLMs) have shown great promise but require substantial computational resources during inference. Attackers can exploit this by inducing excessive…
VideoPure: Diffusion-based Adversarial Purification for Video Recognition
Kaixun Jiang, Zhaoyu Chen, Jiyuan Fu +3
Recent work indicates that video recognition models are vulnerable to adversarial examples, posing a serious security risk to downstream applications. However, current research has…
Boosting Adversarial Transferability with Spatial Adversarial Alignment
Zhaoyu Chen, Haijing Guo, Kaixun Jiang +6
Deep neural networks are vulnerable to adversarial examples that exhibit transferability across various models. Numerous approaches are proposed to enhance the transferability of a…
X-Prompt: Multi-modal Visual Prompt for Video Object Segmentation
Pinxue Guo, Wanyun Li, Hao Huang +7
Multi-modal Video Object Segmentation (VOS), including RGB-Thermal, RGB-Depth, and RGB-Event, has garnered attention due to its capability to address challenging scenarios where tr…
General Compression Framework for Efficient Transformer Object Tracking
Lingyi Hong, Jinglun Li, Xinyu Zhou +10
Previous works have attempted to improve tracking efficiency through lightweight architecture design or knowledge distillation from teacher models to compact student trackers. Howe…
TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning
Jinglun Li, Xinyu Zhou, Kaixun Jiang +5
Multimodal fusion, leveraging data like vision and language, is rapidly gaining traction. This enriched data representation improves performance across various tasks. Existing meth…
Improving Adversarial Transferability with Neighbourhood Gradient Information
Haijing Guo, Jiafeng Wang, Zhaoyu Chen +5
Deep neural networks (DNNs) are known to be susceptible to adversarial examples, leading to significant performance degradation. In black-box attack scenarios, a considerable attac…
PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous Driving
Jiyuan Fu, Zhaoyu Chen, Kaixun Jiang +3
Vision foundation models are increasingly employed in autonomous driving systems due to their advanced capabilities. However, these models are susceptible to adversarial attacks, p…
LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation
Lingyi Hong, Zhongying Liu, Wenchao Chen +9
Video object segmentation (VOS) aims to distinguish and track target objects in a video. Despite the excellent performance achieved by off-the-shell VOS models, existing VOS benchm…
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning
Lingyi Hong, Shilin Yan, Renrui Zhang +8
Visual object tracking aims to localize the target object of each frame based on its initial appearance in the first frame. Depending on the input modility, tracking tasks can be d…
ClickVOS: Click Video Object Segmentation
Pinxue Guo, Lingyi Hong, Xinyu Zhou +7
Video Object Segmentation (VOS) task aims to segment objects in videos. However, previous settings either require time-consuming manual masks of target objects at the first frame d…
Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction
Jiyuan Fu, Zhaoyu Chen, Kaixun Jiang +4
Despite the substantial advancements in Vision-Language Pre-training (VLP) models, their susceptibility to adversarial attacks poses a significant challenge. Existing work rarely s…
Delving into Decision-based Black-box Attacks on Semantic Segmentation
Zhaoyu Chen, Zhengyang Shan, Jingwen Chang +4
Semantic segmentation is a fundamental visual task that finds extensive deployment in applications with security-sensitive considerations. Nonetheless, recent work illustrates the…
Exploring the Adversarial Robustness of Face Forgery Detection with Decision-based Black-box Attacks
Zhaoyu Chen, Bo Li, Kaixun Jiang +3
Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and id…
Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentation
Siao Liu, Zhaoyu Chen, Yang Liu +8
Learning a policy with great generalization to unseen environments remains challenging but critical in visual reinforcement learning. Despite the success of augmentation combinatio…
AIDE: A Vision-Driven Multi-View, Multi-Modal, Multi-Tasking Dataset for Assistive Driving Perception
Dingkang Yang, Shuai Huang, Zhi Xu +12
Driver distraction has become a significant cause of severe traffic accidents over the past decade. Despite the growing development of vision-driven driver monitoring systems, the…
Query-Efficient Decision-based Black-Box Patch Attack
Zhaoyu Chen, Bo Li, Shuang Wu +2
Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce…
Sampling to Distill: Knowledge Transfer from Open-World Data
Yuzheng Wang, Zhaoyu Chen, Jie Zhang +7
Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the pre-trained teacher network without original training data…