17 citations · 64 across the 51 of their papers we have counts for
23 papers · 1 filter
WithEveryone: Unified Planning and Identity Grounding for Group Image Generation
Hengyuan Xu, Qixun Wang, Yiji Cheng +5
Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every refe…
Resolving Representation Ambiguity in Feedforward Novel View Synthesis Transformer via Semantic-Spatial Decoupling
Yihang Wu, Yihang Sun, Shaofeng Zhang +4
Transformer-based models have advanced feedforward novel view synthesis (NVS). Current architectures such as GS-LRM and LVSM mix semantic information (e.g., RGB) and spatial inform…
TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models
Xin Wang, Yixu Wang, Jiaming Zhang +6
Large-scale pre-trained Vision-Language models (VLMs), such as CLIP, exhibit strong zero-shot generalization, yet remain highly vulnerable to imperceptible adversarial perturbation…
BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models
Juncheng Li, Yige Li, Hanxun Huang +5
Backdoor attacks undermine the reliability and trustworthiness of machine learning systems by injecting hidden behaviors that can be maliciously activated at inference time. While…
WithAnyone: Towards Controllable and ID Consistent Image Generation
Hengyuan Xu, Wei Cheng, Peng Xing +8
Identity-consistent generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with a reference id…
FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models
Xin Wang, Jie Li, Zejia Weng +9
Vision-Language-Action (VLA) models are driving rapid progress in robotics by enabling agents to interpret multimodal inputs and execute complex, long-horizon tasks. However, their…
DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning
Yifeng Gao, Yifan Ding, Hongyu Su +9
As AI-generated video becomes increasingly pervasive across media platforms, the ability to reliably distinguish synthetic content from authentic footage has become both urgent and…
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models
Jiaming Zhang, Xin Wang, Xingjun Ma +3
Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capabilities in understanding relationships between visual and textual data through joint embedding spaces.…
SafeVid: Toward Safety Aligned Video Large Multimodal Models
Yixu Wang, Jiaxin Song, Yifeng Gao +6
As Video Large Multimodal Models (VLMMs) rapidly advance, their inherent complexity introduces significant safety challenges, particularly the issue of mismatched generalization wh…
SAMA: Towards Multi-Turn Referential Grounded Video Chat with Large Language Models
Ye Sun, Hao Zhang, Henghui Ding +3
Achieving fine-grained spatio-temporal understanding in videos remains a major challenge for current Video Large Multimodal Models (Video LMMs). Addressing this challenge requires…
OmniSVG: A Unified Scalable Vector Graphics Generation Model
Yiying Yang, Wei Cheng, Sijin Chen +7
Scalable Vector Graphics (SVG) is an important image format widely adopted in graphic design because of their resolution independence and editability. The study of generating high-…
AIM: Additional Image Guided Generation of Transferable Adversarial Attacks
Teng Li, Xingjun Ma, Yu-Gang Jiang
Transferable adversarial examples highlight the vulnerability of deep neural networks (DNNs) to imperceptible perturbations across various real-world applications. While there have…
BadPatch: Diffusion-Based Generation of Physical Adversarial Patches
Zhixiang Wang, Xingjun Ma, Yu-Gang Jiang
Physical adversarial patches printed on clothing can enable individuals to evade person detectors, but most existing methods prioritize attack effectiveness over stealthiness, resu…
IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves
Ruofan Wang, Juncheng Li, Yixu Wang +6
As large Vision-Language Models (VLMs) gain prominence, ensuring their safe deployment has become critical. Recent studies have explored VLM robustness against jailbreak attacks-te…
UnSeg: One Universal Unlearnable Example Generator is Enough against All Image Segmentation
Ye Sun, Hao Zhang, Tiehua Zhang +2
Image segmentation is a crucial vision task that groups pixels within an image into semantically meaningful segments, which is pivotal in obtaining a fine-grained understanding of…
BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks
Yunhan Zhao, Xiang Zheng, Lin Luo +3
In this paper, we focus on black-box defense for VLMs against jailbreak attacks. Existing black-box defense methods are either unimodal or bimodal. Unimodal methods enhance either…
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
Xin Wang, Kai Chen, Xingjun Ma +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based…
Downstream Transfer Attack: Adversarial Attacks on Downstream Models with Pre-trained Vision Transformers
Weijie Zheng, Xingjun Ma, Hanxun Huang +2
With the advancement of vision transformers (ViTs) and self-supervised learning (SSL) techniques, pre-trained large ViTs have become the new foundation models for computer vision a…
A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models
Xincheng Shuai, Henghui Ding, Xingjun Ma +3
Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. It is widely studied in recent years as a promising and challenging field…
Adversarial Prompt Tuning for Vision-Language Models
Jiaming Zhang, Xingjun Ma, Xin Wang +4
With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual…
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
Shihao Zhao, Xingjun Ma, Yisen Wang +3
Deep neural networks (DNNs) are increasingly deployed in different applications to achieve state-of-the-art performance. However, they are often applied as a black box with limited…
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Xingjun Ma, Linxi Jiang, Hanxun Huang +3
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research. Obfuscated gradients have previously been found to exist in many defense meth…
Clean-Label Backdoor Attacks on Video Recognition Models
Shihao Zhao, Xingjun Ma, Xiang Zheng +3
Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor triggers in DNNs by poisoning training data. A backdoored model behaves normally on clean tes…