1 citations · 1 across the 7 of their papers we have counts for
7 papers · 1 filter
Towards Generalizable Deepfake Detection via Real Distribution Bias Correction
Ming-Hui Liu, Harry Cheng, Xin Luo +2
To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using available source domain data. Ho…
Progressively Exploring and Exploiting Inference Data to Break Fine-Grained Classification Barrier
Li-Jun Zhao, Si-Yuan Zhang, Zhen-Duo Chen +2
Current fine-grained classification research primarily focuses on fine-grained feature learning. However, in real-world scenarios, fine-grained data annotation is challenging, and…
EVC-MF: End-to-end Video Captioning Network with Multi-scale Features
Tian-Zi Niu, Zhen-Duo Chen, Xin Luo +1
Conventional approaches for video captioning leverage a variety of offline-extracted features to generate captions. Despite the availability of various offline-feature-extractors t…
Bias Mitigating Few-Shot Class-Incremental Learning
Li-Jun Zhao, Zhen-Duo Chen, Zi-Chao Zhang +2
Few-shot class-incremental learning (FSCIL) aims at recognizing novel classes continually with limited novel class samples. A mainstream baseline for FSCIL is first to train the wh…
Federated Class-Incremental Learning with Prompting
Xin Luo, Fang-Yi Liang, Jiale Liu +3
As Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread atte…
FedVMR: A New Federated Learning method for Video Moment Retrieval
Yan Wang, Xin Luo, Zhen-Duo Chen +3
Despite the great success achieved, existing video moment retrieval (VMR) methods are developed under the assumption that data are centralizedly stored. However, in real-world appl…