3 citations · 3 across the 15 of their papers we have counts for
15 papers
InfraPatch: Cross-Task Targeted Grayscale Patch Attacks on Infrared-Adapted Vision-Language Models
Chengyin Hu, Dingyi Lu, Jiaju Han +5
Infrared vision-language models (IR-VLMs) have emerged as a promising paradigm for multimodal perception under low-visibility conditions, yet their robustness to targeted adversari…
Radar4D-VLM: Proposal-Grounded Temporal 4D Radar Reasoning Across Frozen Language Models
Jiaju Han, Xuemeng Sun, Qike Zhang +6
Vision-language models for autonomous driving primarily rely on cameras and LiDAR, leaving 4D radar largely unexplored as a standalone perceptual modality despite its robustness to…
QR-Structured Thermal Triggers for Targeted Semantic Attacks on Infrared Vision-Language Models
Xiang Chen, Yingying Zhao, Chao Li +7
Infrared vision-language models (IR-VLMs) extend thermal perception to open-vocabulary classification, image captioning, and visual question answering. However, their robustness to…
MonoIR-RS: Infrared Remote Sensing Vision-Language Learning with CLIP and VLM Adaptation
Jiaju Han, Ma Yaqi, Yahui Chai +8
Infrared remote-sensing imagery captures intensity structure, object-background contrast, and illumination-invariant cues often invisible in RGB imagery. Yet, most remote-sensing v…
Exposing Vulnerabilities in Visible-Infrared VLMs: A Unified Geometric Adversarial Framework with Cross-Task Transferability
Xiang Chen, Yuxian Dong, Chao Li +6
Vision-language models (VLMs) have achieved strong performance across diverse multimodal tasks, but their adversarial robustness in visible-infrared (VIS-IR) scenarios remains unde…
When Surfaces Lie: Exploiting Wrinkle-Induced Attention Shift to Attack Vision-Language Models
Chengyin Hu, Xuemeng Sun, Jiaju Han +5
Visual-Language Models (VLMs) have demonstrated exceptional cross-modal understanding across various tasks, including zero-shot classification, image captioning, and visual questio…
Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patch for Infrared Vision-Language Models
Chengyin Hu, Yuxian Dong, Yikun Guo +8
Infrared vision-language models (IR-VLMs) are becoming important for semantic perception in low-visibility environments, yet their robustness to physical semantic attacks remains u…
AuroraEdge-V-2B: A Faster And Stronger Edge Visual Large Language Model
Xiang Chen
Recently, due to the advancement of multimodal technology, people are attempting to use visual large language models (VLLMs) in industrial production. Many deep learning models (DL…
A Semantic Decoupling-Based Two-Stage Rainy-Day Attack for Revealing Weather Robustness Deficiencies in Vision-Language Models
Chengyin Hu, Xiang Chen, Zhe Jia +4
Vision-Language Models (VLMs) are trained on image-text pairs collected under canonical visual conditions and achieve strong performance on multimodal tasks. However, their robustn…
VUGEN: Visual Understanding priors for GENeration
Xiangyi Chen, Théophane Vallaeys, Maha Elbayad +2
Recent advances in Vision-Language Models (VLMs) have enabled unified understanding across text and images, yet equipping these models with robust image generation capabilities rem…
DeLiVR: Differential Spatiotemporal Lie Bias for Efficient Video Deraining
Shuning Sun, Jialang Lu, Xiang Chen +5
Videos captured in the wild often suffer from rain streaks, blur, and noise. In addition, even slight changes in camera pose can amplify cross-frame mismatches and temporal artifac…
HumanOmni: A Large Vision-Speech Language Model for Human-Centric Video Understanding
Jiaxing Zhao, Qize Yang, Yixing Peng +8
In human-centric scenes, the ability to simultaneously understand visual and auditory information is crucial. While recent omni models can process multiple modalities, they general…
Facial Dynamics in Video: Instruction Tuning for Improved Facial Expression Perception and Contextual Awareness
Jiaxing Zhao, Boyuan Sun, Xiang Chen +1
Facial expression captioning has found widespread application across various domains. Recently, the emergence of video Multimodal Large Language Models (MLLMs) has shown promise in…
LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding
Boyuan Sun, Jiaxing Zhao, Xiang Chen +2
In this paper, we introduce LLaVA-Octopus, a novel video multimodal large language model. LLaVA-Octopus adaptively weights features from different visual projectors based on user i…
Face Aging via Diffusion-based Editing
Xiangyi Chen, Stéphane Lathuilière
In this paper, we address the problem of face aging: generating past or future facial images by incorporating age-related changes to the given face. Previous aging methods rely sol…