2 citations · 4 across the 30 of their papers we have counts for
31 papers
Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents
Zhaoyuan Huang, Tianjie Ju, Pengzhou Cheng +7
Graphical user interface (GUI) agents are increasingly used to execute natural-language instructions on user interfaces, yet real users may issue infeasible instructions due to ben…
ArmorOCR: Grounded Adversarial Visual Perception via Observation-Transferred Self-Distillation
Linhan Cao, Siyuan Li, Jun Lan +8
Large multimodal models (LMMs) have demonstrated strong OCR recognition capabilities, yet remain vulnerable to adversarial visual text that is readable to humans but challenging fo…
Veritas++: Value-aware On-Policy Distillation for Perception-Enhanced AIGI Detection
Hao Tan, Jun Lan, Zichang Tan +7
The growing capability of image generation models has made synthetic images a routine presence in open media, making robust and generalizable AI-Generated Image (AIGI) detection in…
RoCo-ACE: Rollout-Conditioned Online Distillation for Retention-Aware Knowledge Injection
Yan Hong, Wei Li, Kedong Xiu +6
Knowledge injection updates pretrained MLLMs with new factual or domain-specific knowledge, but fitting full authoritative answers can cause drift in non-updated behavior. Online d…
LoMC: Localized Multidirectional Correction for Refusal Suppression in Routed Foundation Models
Yan Hong, Kedong Xiu, Wei Li +6
We study controlled post-training refusal suppression in routed MoE and hybrid-MoE foundation models, aiming to increase non-refusal target-response behavior while preserving gener…
Causal Probing for Internal Visual Representations in Multimodal Large Language Models
Zehao Deng, Tianjie Ju, Zheng Wu +5
Despite the remarkable success of Multimodal Large Language Models (MLLMs) across diverse tasks, the internal mechanisms governing how they encode and ground distinct visual concep…
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
Lai Wei, Liangbo He, Jun Lan +9
Multimodal Large Language Models (MLLMs) excel at broad visual understanding but still struggle with fine-grained perception, where decisive evidence is small and easily overwhelme…
VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning
Hao Tan, Jun Lan, Senyuan Shi +6
The growing capability of video generation poses escalating security risks, making reliable detection increasingly essential. In this paper, we introduce VideoVeritas, a framework…
Adaptive and Balanced Re-initialization for Long-timescale Continual Test-time Domain Adaptation
Yanshuo Wang, Jinguang Tong, Jun Lan +5
Continual test-time domain adaptation (CTTA) aims to adjust models so that they can perform well over time across non-stationary environments. While previous methods have made cons…
Generalizable and Adaptive Continual Learning Framework for AI-generated Image Detection
Hanyi Wang, Jun Lan, Yaoyu Kang +4
The malicious misuse and widespread dissemination of AI-generated images pose a significant threat to the authenticity of online information. Current detection methods often strugg…
Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images
Yikun Ji, Yan Hong, Bowen Deng +5
The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising practical concerns for digital integrity. Vision-language models (VLMs…
GAMMA: Generalizable Alignment via Multi-task and Manipulation-Augmented Training for AI-Generated Image Detection
Haozhen Yan, Yan Hong, Suning Lang +6
With generative models becoming increasingly sophisticated and diverse, detecting AI-generated images has become increasingly challenging. While existing AI-genereted Image detecto…
Generalizable Audio Deepfake Detection via Hierarchical Structure Learning and Feature Whitening in Poincaré sphere
Mingru Yang, Yanmei Gu, Qianhua He +7
Audio deepfake detection (ADD) faces critical generalization challenges due to diverse real-world spoofing attacks and domain variations. However, existing methods primarily rely o…
Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning
Hao Tan, Jun Lan, Zichang Tan +7
Deepfake detection remains a formidable challenge due to the complex and evolving nature of fake content in real-world scenarios. However, existing academic benchmarks suffer from…
EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO
Wei Guan, Jun Lan, Jian Cao +3
Industrial anomaly detection (IAD) plays a crucial role in maintaining the safety and reliability of manufacturing systems. While multimodal large language models (MLLMs) show stro…
Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs
Yikun Ji, Hong Yan, Jun Lan +5
The rapid advancement of image generation technologies intensifies the demand for interpretable and robust detection methods. Although existing approaches often attain high accurac…
DS-VTON: An Enhanced Dual-Scale Coarse-to-Fine Framework for Virtual Try-On
Xianbing Sun, Yan Hong, Jiahui Zhan +5
Despite recent progress, most existing virtual try-on methods still struggle to simultaneously address two core challenges: accurately aligning the garment image with the target hu…
Robustness in AI-Generated Detection: Enhancing Resistance to Adversarial Attacks
Sun Haoxuan, Hong Yan, Zhan Jiahui +6
The rapid advancement of generative image technology has introduced significant security concerns, particularly in the domain of face generation detection. This paper investigates…
Benchmarking Unified Face Attack Detection via Hierarchical Prompt Tuning
Ajian Liu, Haocheng Yuan, Xiao Guo +13
PAD and FFD are proposed to protect face data from physical media-based Presentation Attacks and digital editing-based DeepFakes, respectively. However, isolated training of these…
Keep the General, Inject the Specific: Structured Dialogue Fine-Tuning for Knowledge Injection without Catastrophic Forgetting
Yijie Hong, Xiaofei Yin, Xinzhong Wang +7
Large Vision Language Models have demonstrated impressive versatile capabilities through extensive multimodal pre-training, but face significant limitations when incorporating spec…
Towards Explainable Fake Image Detection with Multi-Modal Large Language Models
Yikun Ji, Yan Hong, Jiahui Zhan +6
Progress in image generation raises significant public security concerns. We argue that fake image detection should not operate as a "black box". Instead, an ideal approach must en…
InterAnimate: Taming Region-aware Diffusion Model for Realistic Human Interaction Animation
Yukang Lin, Yan Hong, Zunnan Xu +10
Recent video generation research has focused heavily on isolated actions, leaving interactive motions-such as hand-face interactions-largely unexamined. These interactions are esse…
InsightVision: A Comprehensive, Multi-Level Chinese-based Benchmark for Evaluating Implicit Visual Semantics in Large Vision Language Models
Xiaofei Yin, Yijie Hong, Ya Guo +4
In the evolving landscape of multimodal language models, understanding the nuanced meanings conveyed through visual cues - such as satire, insult, or critique - remains a significa…
Maintain Plasticity in Long-timescale Continual Test-time Adaptation
Yanshuo Wang, Xuesong Li, Jinguang Tong +5
Continual test-time domain adaptation (CTTA) aims to adjust pre-trained source models to perform well over time across non-stationary target environments. While previous methods ha…