1 citations · 2 across the 18 of their papers we have counts for
4 papers · 1 filter
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…
Modality Alignment Meets Federated Broadcasting
Yuting Ma, Shengeng Tang, Xiaohua Xu +1
Federated learning (FL) has emerged as a powerful approach to safeguard data privacy by training models across distributed edge devices without centralizing local data. Despite adv…
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…