6 citations · 9 across the 18 of their papers we have counts for
12 papers · 1 filter
Watch, Remember, Reason: Human-View Video Understanding with MLLMs
Jiahao Meng, Yue Tan, Qi Xu +12
Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video…
Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection
Tianxiao Li, Zhenglin Huang, Haiquan Wen +10
Multimodal deepfakes are proliferating on social media and threaten authenticity, information integrity, and digital forensics. Existing benchmarks are constrained by their single-…
Rethinking Cross-Generator Image Forgery Detection through DINOv3
Zhenglin Huang, Jason Li, Haiquan Wen +7
As generative models become increasingly diverse and powerful, cross-generator detection has emerged as a new challenge. Existing detection methods often memorize artifacts of spec…
Spatial-DISE: A Unified Benchmark for Evaluating Spatial Reasoning in Vision-Language Models
Xinmiao Huang, Qisong He, Zhenglin Huang +5
Spatial reasoning ability is crucial for Vision Language Models (VLMs) to support real-world applications in diverse domains including robotics, augmented reality, and autonomous n…
RAIDX: A Retrieval-Augmented Generation and GRPO Reinforcement Learning Framework for Explainable Deepfake Detection
Tianxiao Li, Zhenglin Huang, Haiquan Wen +4
The rapid advancement of AI-generation models has enabled the creation of hyperrealistic imagery, posing ethical risks through widespread misinformation. Current deepfake detection…
BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM
Haiquan Wen, Tianxiao Li, Zhenglin Huang +2
The rapid advancement of generative AI has substantially improved image and video synthesis, amplifying the risk of multimodal visual misinformation. Recent MLLMs have shown promis…
Towards Explainable Bilingual Multimodal Misinformation Detection and Localization
Yiwei He, Zhenglin Huang, Haiquan Wen +5
The increasing realism of multimodal content has made misinformation more subtle and harder to detect, especially in news media where images are frequently paired with bilingual (e…
So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection
Zhenglin Huang, Xiangtai Li, Xi Yang +6
Recent advances in AI-powered generative models have enabled the creation of increasingly realistic synthetic images, posing significant risks to information integrity and public t…
BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation
Haiquan Wen, Yiwei He, Zhenglin Huang +7
As generative video models become increasingly realistic, detecting AI-generated videos requires systems that offer both accuracy and interpretability. However, applying Multimodal…
Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing
Sihao Wu, Xiaonan Si, Chi Xing +5
The integration of preference alignment with diffusion models (DMs) has emerged as a transformative approach to enhance image generation and editing capabilities. Although integrat…
A Black-Box Evaluation Framework for Semantic Robustness in Bird's Eye View Detection
Fu Wang, Yanghao Zhang, Xiangyu Yin +4
Camera-based Bird's Eye View (BEV) perception models receive increasing attention for their crucial role in autonomous driving, a domain where concerns about the robustness and rel…
SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model
Zhenglin Huang, Jinwei Hu, Xiangtai Li +6
The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when share…