60 citations · 78 across the 18 of their papers we have counts for
18 papers
PATE-Forensics: Perception-as-Tool for Explainable Deepfake Forensics with General-Purpose MLLMs
Yaqi Li, Jielun Peng, Yabin Wang +2
Existing explainable deepfake forensic methods typically rely on task-adapted MLLM to jointly address detection, localization, and explanation. Inspired by agent-style tool use, we…
A Benchmark for Semi-supervised Multi-modal Crowd Counting
Haoliang Meng, Xiaopeng Hong, Yabin Wang +1
This paper constructs the first benchmark on semi-supervised multi-modal crowd counting. To lay the foundation for this unexplored task, we first formulate the semi-supervised mult…
Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging
Zhilin Zhu, Yabin Wang, Zhiheng Ma +3
Continual Test-Time Adaptation (CTTA) aims to empower perception systems to handle dynamic distribution shifts encountered after deployment. Existing methods predominantly follow a…
Robust Deepfake Detection, NTIRE 2026 Challenge: Report
Benedikt Hopf, Radu Timofte, Chenfan Qu +54
Robustness is a long-overlooked problem in deepfake detection. However, detection performance is nearly worthless in the real world if it suffers under exposure to even slight imag…
Leave No Stone Unturned: Uncovering Holistic Audio-Visual Intrinsic Coherence for Deepfake Detection
Jielun Peng, Yabin Wang, Yaqi Li +2
The rapid progress of generative AI has enabled hyper-realistic audio-visual deepfakes, intensifying threats to personal security and social trust. Most existing deepfake detectors…
OpenSDI: Spotting Diffusion-Generated Images in the Open World
Yabin Wang, Zhiwu Huang, Xiaopeng Hong
This paper identifies OpenSDI, a challenge for spotting diffusion-generated images in open-world settings. In response to this challenge, we define a new benchmark, the OpenSDI dat…
A Benchmark for Incremental Micro-expression Recognition
Zhengqin Lai, Xiaopeng Hong, Yabin Wang +1
Micro-expression recognition plays a pivotal role in understanding hidden emotions and has applications across various fields. Traditional recognition methods assume access to all…
Penny-Wise and Pound-Foolish in Deepfake Detection
Yabin Wang, Zhiwu Huang, Su Zhou +2
The diffusion of deepfake technologies has sparked serious concerns about its potential misuse across various domains, prompting the urgent need for robust detection methods. Despi…
Multi-modal Crowd Counting via Modal Emulation
Chenhao Wang, Xiaopeng Hong, Zhiheng Ma +3
Multi-modal crowd counting is a crucial task that uses multi-modal cues to estimate the number of people in crowded scenes. To overcome the gap between different modalities, we pro…
Prompt Customization for Continual Learning
Yong Dai, Xiaopeng Hong, Yabin Wang +3
Contemporary continual learning approaches typically select prompts from a pool, which function as supplementary inputs to a pre-trained model. However, this strategy is hindered b…
Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
Yabin Wang, Zhiwu Huang, Zhiheng Ma +1
The emergence of text-to-image generative models has revolutionized the field of deepfakes, enabling the creation of realistic and convincing visual content directly from textual d…
Towards Practical Multi-Robot Hybrid Tasks Allocation for Autonomous Cleaning
Yabin Wang, Xiaopeng Hong, Zhiheng Ma +3
Task allocation plays a vital role in multi-robot autonomous cleaning systems, where multiple robots work together to clean a large area. However, most current studies mainly focus…
Remind of the Past: Incremental Learning with Analogical Prompts
Zhiheng Ma, Xiaopeng Hong, Beinan Liu +3
Although data-free incremental learning methods are memory-friendly, accurately estimating and counteracting representation shifts is challenging in the absence of historical data.…
Benchmarking Deepart Detection
Yabin Wang, Zhiwu Huang, Xiaopeng Hong
Deepfake technologies have been blurring the boundaries between the real and unreal, likely resulting in malicious events. By leveraging newly emerged deepfake technologies, deepfa…
Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interference
Yabin Wang, Zhiheng Ma, Zhiwu Huang +3
This paper focuses on the prevalent performance imbalance in the stages of incremental learning. To avoid obvious stage learning bottlenecks, we propose a brand-new stage-isolation…
S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learning
Yabin Wang, Zhiwu Huang, Xiaopeng Hong
State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (name…
A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
Chuqiao Li, Zhiwu Huang, Danda Pani Paudel +4
There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in t…
Object Counting: You Only Need to Look at One
Hui Lin, Xiaopeng Hong, Yabin Wang
This paper aims to tackle the challenging task of one-shot object counting. Given an image containing novel, previously unseen category objects, the goal of the task is to count al…