1 citations · 4 across the 9 of their papers we have counts for
9 papers
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…
ASAP: Advancing Semantic Alignment Promotes Multi-Modal Manipulation Detecting and Grounding
Zhenxing Zhang, Yaxiong Wang, Lechao Cheng +3
We present ASAP, a new framework for detecting and grounding multi-modal media manipulation (DGM4).Upon thorough examination, we observe that accurate fine-grained cross-modal sema…
Dataset Distillers Are Good Label Denoisers In the Wild
Lechao Cheng, Kaifeng Chen, Jiyang Li +3
Learning from noisy data has become essential for adapting deep learning models to real-world applications. Traditional methods often involve first evaluating the noise and then ap…
EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning
Yaxiong Wang, Yujiao Wu, Lianwei Wu +3
Recent advancements in image-text matching have been notable, yet prevailing models predominantly cater to broad queries and struggle with accommodating fine-grained query intentio…
FedHPL: Efficient Heterogeneous Federated Learning with Prompt Tuning and Logit Distillation
Yuting Ma, Lechao Cheng, Yaxiong Wang +3
Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed clients to collaboratively train models with a central server while keeping raw data local…
Revisiting the Power of Prompt for Visual Tuning
Yuzhu Wang, Lechao Cheng, Chaowei Fang +3
Visual prompt tuning (VPT) is a promising solution incorporating learnable prompt tokens to customize pre-trained models for downstream tasks. However, VPT and its variants often e…