1 citations · 2 across the 18 of their papers we have counts for
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TDEdit: A Unified Diffusion Framework for Text-Drag Guided Image Manipulation
Qihang Wang, Yaxiong Wang, Lechao Cheng +1
This paper explores image editing under the joint control of text and drag interactions. While recent advances in text-driven and drag-driven editing have achieved remarkable progr…
Beyond Artificial Misalignment: Detecting and Grounding Semantic-Coordinated Multimodal Manipulations
Jinjie Shen, Yaxiong Wang, Lechao Cheng +2
The detection and grounding of manipulated content in multimodal data has emerged as a critical challenge in media forensics. While existing benchmarks demonstrate technical progre…
Towards Unified Multimodal Misinformation Detection in Social Media: A Benchmark Dataset and Baseline
Haiyang Li, Yaxiong Wang, Shengeng Tang +6
Detecting deceptive multimodal content on social media has become an increasingly important problem. Two major types of deception dominate: human-crafted misinformation (e.g., rumo…
FakeSV-VLM: Taming VLM for Detecting Fake Short-Video News via Progressive Mixture-Of-Experts Adapter
Junxi Wang, Yaxiong Wang, Lechao Cheng +1
We present FakeSV-VLM in this paper, a new VLM-based framework for detecting fake news on short video platforms. Despite significant efforts to combat this issue due to the severe…
SSAM: Self-Supervised Association Modeling for Test-Time Adaption
Yaxiong Wang, Zhenqiang Zhang, Lechao Cheng +3
Test-time adaption (TTA) has witnessed important progress in recent years, the prevailing methods typically first encode the image and the text and design strategies to model the a…
Towards Micro-Action Recognition with Limited Annotations: An Asynchronous Pseudo Labeling and Training Approach
Yan Zhang, Lechao Cheng, Yaxiong Wang +2
Micro-Action Recognition (MAR) aims to classify subtle human actions in video. However, annotating MAR datasets is particularly challenging due to the subtlety of actions. To this…
Knowledge Swapping via Learning and Unlearning
Mingyu Xing, Lechao Cheng, Shengeng Tang +3
We introduce \textbf{Knowledge Swapping}, a novel task designed to selectively regulate knowledge of a pretrained model by enabling the forgetting of user\-specified information, r…
Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning
Fangwen Wu, Lechao Cheng, Shengeng Tang +4
Class-incremental learning (CIL) seeks to enable a model to sequentially learn new classes while retaining knowledge of previously learned ones. Balancing flexibility and stability…