6 citations · 16 across the 19 of their papers we have counts for
8 papers · 1 filter
Refining Open-Vocabulary Semantic Segmentation via Regional Semantics and Visual Prototypes
Aijing Yu, Zhengbo Wang, Lijun Sheng +2
Contrastive Language-Image Pre-training (CLIP) has shown superior performance in the image-level open-vocabulary classification task, benefiting from its powerful alignment of visu…
Cooperative Pseudo Labeling for Unsupervised Federated Classification
Kuangpu Guo, Lijun Sheng, Yongcan Yu +3
Unsupervised Federated Learning (UFL) aims to collaboratively train a global model across distributed clients without sharing data or accessing label information. Previous UFL work…
Towards Compatible Fine-tuning for Vision-Language Model Updates
Zhengbo Wang, Jian Liang, Lijun Sheng +3
So far, efficient fine-tuning has become a popular strategy for enhancing the capabilities of foundation models on downstream tasks by learning plug-and-play modules. However, exis…
Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
Yuting Xu, Jian Liang, Lijun Sheng +1
The deepfake threats to society and cybersecurity have provoked significant public apprehension, driving intensified efforts within the realm of deepfake video detection. Current v…
A Hard-to-Beat Baseline for Training-free CLIP-based Adaptation
Zhengbo Wang, Jian Liang, Lijun Sheng +3
Contrastive Language-Image Pretraining (CLIP) has gained popularity for its remarkable zero-shot capacity. Recent research has focused on developing efficient fine-tuning methods,…
Self-training solutions for the ICCV 2023 GeoNet Challenge
Lijun Sheng, Zhengbo Wang, Jian Liang
GeoNet is a recently proposed domain adaptation benchmark consisting of three challenges (i.e., GeoUniDA, GeoImNet, and GeoPlaces). Each challenge contains images collected from th…
Realistic Unsupervised CLIP Fine-tuning with Universal Entropy Optimization
Jian Liang, Lijun Sheng, Zhengbo Wang +2
The emergence of vision-language models, such as CLIP, has spurred a significant research effort towards their application for downstream supervised learning tasks. Although some p…
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation
Yuhe Ding, Lijun Sheng, Jian Liang +2
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Owing to privacy concerns and heavy data transmission, s…