most citedGeneralized Few-Shot Continual Learning with Contrastive Mixture of Adapters

5 citations · 24 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CV20233 cited

Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection

Anwei Luo, Chenqi Kong, Jiwu Huang +3

Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as n…

cs.CV20231 cited

Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less

Rizhao Cai, Yawen Cui, Zhi Li +4

Face Anti-Spoofing (FAS) is recently studied under the continual learning setting, where the FAS models are expected to evolve after encountering the data from new domains. However…

eess.IV2023

Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation

Ziwang Xu, Lanqing Guo, Shuyan Zhang +2

Staining is critical to cell imaging and medical diagnosis, which is expensive, time-consuming, labor-intensive, and causes irreversible changes to cell tissues. Recent advances in…

cs.CV20232 cited

Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

Yi Yu, Yufei Wang, Wenhan Yang +3

Recent deep-learning-based compression methods have achieved superior performance compared with traditional approaches. However, deep learning models have proven to be vulnerable t…

cs.CV20233 cited

Temporal Coherent Test-Time Optimization for Robust Video Classification

Chenyu Yi, Siyuan Yang, Yufei Wang +3

Deep neural networks are likely to fail when the test data is corrupted in real-world deployment (e.g., blur, weather, etc.). Test-time optimization is an effective way that adapts…

cs.CV2023

Raw Image Reconstruction with Learned Compact Metadata

Yufei Wang, Yi Yu, Wenhan Yang +4

While raw images exhibit advantages over sRGB images (e.g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requir…