538 citations · 563 across the 21 of their papers we have counts for
16 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…
Suppressing Gradient Conflict for Generalizable Deepfake Detection
Ming-Hui Liu, Harry Cheng, Xin Luo +1
Robust deepfake detection models must be capable of generalizing to ever-evolving manipulation techniques beyond training data. A promising strategy is to augment the training data…
Learning Real Facial Concepts for Independent Deepfake Detection
Ming-Hui Liu, Harry Cheng, Tianyi Wang +2
Deepfake detection models often struggle with generalization to unseen datasets, manifesting as misclassifying real instances as fake in target domains. This is primarily due to an…
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution
Ming-Hui Liu, Xiao-Qian Liu, Xin Luo +1
Deepfake attribution (DFA) aims to perform multiclassification on different facial manipulation techniques, thereby mitigating the detrimental effects of forgery content on the soc…
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…
Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models
Yu-Wei Zhan, Fan Liu, Xin Luo +3
Human-Object Interaction (HOI) detection aims at detecting human-object pairs and predicting their interactions. However, conventional HOI detection methods often struggle to fully…
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…
Three-Stream Joint Network for Zero-Shot Sketch-Based Image Retrieval
Yu-Wei Zhan, Xin Luo, Yongxin Wang +2
The Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) is a challenging task because of the large domain gap between sketches and natural images as well as the semantic inconsistency…
ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator
Zi-Chao Zhang, Zhen-Duo Chen, Yongxin Wang +2
Recently, several Vision Transformer (ViT) based methods have been proposed for Fine-Grained Visual Classification (FGVC).These methods significantly surpass existing CNN-based one…
Weakly-Supervised Online Hashing
Yu-Wei Zhan, Xin Luo, Yu Sun +3
With the rapid development of social websites, recent years have witnessed an explosive growth of social images with user-provided tags which continuously arrive in a streaming fas…
Deep Recurrent Quantization for Generating Sequential Binary Codes
Jingkuan Song, Xiaosu Zhu, Lianli Gao +3
Quantization has been an effective technology in ANN (approximate nearest neighbour) search due to its high accuracy and fast search speed. To meet the requirement of different app…
Measuring the Accuracy of Object Detectors and Trackers
Tobias Bottger, Patrick Follmann, Michael Fauser
The accuracy of object detectors and trackers is most commonly evaluated by the Intersection over Union (IoU) criterion. To date, most approaches are restricted to axis-aligned or…
Optimized Cartesian -Means
Jianfeng Wang, Jingdong Wang, Jingkuan Song +3
Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product…