162 citations · 211 across the 28 of their papers we have counts for
29 papers
MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories
Haojin He, Hao Tan, Zichang Tan +2
Modern AI video generation models can produce videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does not necessarily imply physica…
Unleashing the Potential of Vision-Language Models for Generalizable AI-Generated Image Detection
Weihan Cai, Hao Tan, Zichang Tan +2
Recent work has shown that a simple linear probe on frozen representations from modern vision foundation models (VFMs) can achieve state-of-the-art AIGI detection performance, subs…
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…
HydraPrompt: An Adaptive and Asymmetric Framework of Vision-Language Models for Synthetic Image Detection
Senyuan Shi, Hao Tan, Zichang Tan +4
The rapid evolution of generative models has precipitated a proliferation of fabricated content, posing significant challenges to existing Synthetic Image Detection (SID) methods.…
Reduce the Artifacts Bias for More Generalizable AI-Generated Image Detection
Yiheng Li, Yang Yang, Wenhao Wang +4
As the misuse of AI-generated images grows, generalizable image detection techniques are urgently needed. Recent state-of-the-art (SOTA) methods adopt aligned training datasets to…
CTForensics: A Comprehensive Dataset and Method for AI-Generated CT Image Detection
Yiheng Li, Zichang Tan, Guoqing Xu +3
Recent advances in generative AI have made synthetic Computed Tomography (CT) images increasingly realistic, enabling promising applications in medical data augmentation while rais…
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…
One Ring to Rule Them All: Unifying Group-Based RL via Dynamic Power-Mean Geometry
Weisong Zhao, Tong Wang, Zichang Tan +11
Group-based reinforcement learning has evolved from the arithmetic mean of GRPO to the geometric mean of GMPO. While GMPO improves stability by constraining a conservative objectiv…
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…
Towards Generalizable AI-Generated Image Detection via Image-Adaptive Prompt Learning
Yiheng Li, Zichang Tan, Guoqing Xu +3
In AI-generated image detection, current cutting-edge methods typically adapt pre-trained foundation models through partial-parameter fine-tuning. However, these approaches often s…
Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation
Yiheng Li, Yang Yang, Zichang Tan +4
To tackle the threat of fake news, the task of detecting and grounding multi-modal media manipulation DGM4 has received increasing attention. However, most state-of-the-art methods…
Recover and Match: Open-Vocabulary Multi-Label Recognition through Knowledge-Constrained Optimal Transport
Hao Tan, Zichang Tan, Jun Li +3
Identifying multiple novel classes in an image, known as open-vocabulary multi-label recognition, is a challenging task in computer vision. Recent studies explore the transfer of p…
SSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition
Hao Tan, Zichang Tan, Jun Li +3
Multi-label image recognition is a fundamental task in computer vision. Recently, Vision-Language Models (VLMs) have made notable advancements in this area. However, previous metho…
PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition
Hao Tan, Zichang Tan, Jun Li +2
Multi-label image recognition is a fundamental task in computer vision. Recently, vision-language models have made notable advancements in this area. However, previous methods ofte…
Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation
Huan Liu, Qiang Chen, Zichang Tan +9
In this paper, we study the problem of end-to-end multi-person pose estimation. State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e.…
General vs. Long-Tailed Age Estimation: An Approach to Kill Two Birds with One Stone
Zenghao Bao, Zichang Tan, Jun Li +3
Facial age estimation has received a lot of attention for its diverse application scenarios. Most existing studies treat each sample equally and aim to reduce the average estimatio…
NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognition
Zichang Tan, Jun Li, Jinhao Du +3
Long-tailed visual recognition has received increasing attention in recent years. Due to the extremely imbalanced data distribution in long-tailed learning, the learning process sh…
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing
Ajian Liu, Zichang Tan, Zitong Yu +7
The availability of handy multi-modal (i.e., RGB-D) sensors has brought about a surge of face anti-spoofing research. However, the current multi-modal face presentation attack dete…
Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes
Jiang-Tian Zhai, Ze Feng, Jinhao Du +6
Modern autonomous driving systems are typically divided into three main tasks: perception, prediction, and planning. The planning task involves predicting the trajectory of the ego…
Task-Oriented Multi-Modal Mutual Leaning for Vision-Language Models
Sifan Long, Zhen Zhao, Junkun Yuan +5
Prompt learning has become one of the most efficient paradigms for adapting large pre-trained vision-language models to downstream tasks. Current state-of-the-art methods, like CoO…
Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition
Fanglei Xue, Qiangchang Wang, Zichang Tan +2
Facial Expression Recognition (FER) in the wild is an extremely challenging task. Recently, some Vision Transformers (ViT) have been explored for FER, but most of them perform infe…
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition
Fanglei Xue, Zichang Tan, Yu Zhu +2
Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) t…
Nested Collaborative Learning for Long-Tailed Visual Recognition
Jun Li, Zichang Tan, Jun Wan +2
The networks trained on the long-tailed dataset vary remarkably, despite the same training settings, which shows the great uncertainty in long-tailed learning. To alleviate the unc…
Defending Black-box Skeleton-based Human Activity Classifiers
He Wang, Yunfeng Diao, Zichang Tan +1
Skeletal motions have been heavily replied upon for human activity recognition (HAR). Recently, a universal vulnerability of skeleton-based HAR has been identified across a variety…