32 citations · 71 across the 12 of their papers we have counts for
13 papers
RealWeather: Realistic and Scene-Faithful Weather Translation with Driving World Models
Yuwei Ning, Liangzhi Wang, Yi Xiao +5
Realistic weather translation is valuable for developing and evaluating autonomous driving systems, yet collecting paired videos of the same scenes under different weather conditio…
FakeRadar: Probing Forgery Outliers to Detect Unknown Deepfake Videos
Zhaolun Li, Jichang Li, Yinqi Cai +4
In this paper, we propose FakeRadar, a novel deepfake video detection framework designed to address the challenges of cross-domain generalization in real-world scenarios. Existing…
Mobile-Agent-RAG: Driving Smart Multi-Agent Coordination with Contextual Knowledge Empowerment for Long-Horizon Mobile Automation
Yuxiang Zhou, Jichang Li, Yanhao Zhang +2
Mobile agents show immense potential, yet current state-of-the-art (SoTA) agents exhibit inadequate success rates on real-world, long-horizon, cross-application tasks. We attribute…
DeepShield: Fortifying Deepfake Video Detection with Local and Global Forgery Analysis
Yinqi Cai, Jichang Li, Zhaolun Li +5
Recent advances in deep generative models have made it easier to manipulate face videos, raising significant concerns about their potential misuse for fraud and misinformation. Exi…
Style-Preserving Lip Sync via Audio-Aware Style Reference
Weizhi Zhong, Jichang Li, Yinqi Cai +4
Audio-driven lip sync has recently drawn significant attention due to its widespread application in the multimedia domain. Individuals exhibit distinct lip shapes when speaking the…
Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection
Jiaming Li, Jiacheng Zhang, Jichang Li +4
Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowl…
AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning
Duojun Huang, Xinyu Xiong, Jie Ma +4
Powered by massive curated training data, Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prom…
Inter-Domain Mixup for Semi-Supervised Domain Adaptation
Jichang Li, Guanbin Li, Yizhou Yu
Semi-supervised domain adaptation (SSDA) aims to bridge source and target domain distributions, with a small number of target labels available, achieving better classification perf…
Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation
Jichang Li, Guanbin Li, Yizhou Yu
Compared to unsupervised domain adaptation, semi-supervised domain adaptation (SSDA) aims to significantly improve the classification performance and generalization capability of t…
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
Jichang Li, Guanbin Li, Hui Cheng +2
Federated learning with noisy labels (F-LNL) aims at seeking an optimal server model via collaborative distributed learning by aggregating multiple client models trained with local…
Divide and Adapt: Active Domain Adaptation via Customized Learning
Duojun Huang, Jichang Li, Weikai Chen +3
Active domain adaptation (ADA) aims to improve the model adaptation performance by incorporating active learning (AL) techniques to label a maximally-informative subset of target s…
Neighborhood Collective Estimation for Noisy Label Identification and Correction
Jichang Li, Guanbin Li, Feng Liu +1
Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. The ke…
Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation
Jichang Li, Guanbin Li, Yemin Shi +1
In semi-supervised domain adaptation, a few labeled samples per class in the target domain guide features of the remaining target samples to aggregate around them. However, the tra…