1 citations · 1 across the 7 of their papers we have counts for
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Inductive Convolution Nuclear Norm Minimization for Tensor Completion with Arbitrary Sampling
Wei Li, Yuyang Li, Kaile Du +2
The recently established Convolution Nuclear Norm Minimization (CNNM) addresses the problem of \textit{tensor completion with arbitrary sampling} (TCAS), which involves restoring a…
ZeroDiff++: Substantial Unseen Visual-semantic Correlation in Zero-shot Learning
Zihan Ye, Shreyank N Gowda, Kaile Du +2
Zero-shot Learning (ZSL) enables classifiers to recognize classes unseen during training, commonly via generative two stage methods: (1) learn visual semantic correlations from see…
DeCLIP: Decoupled Prompting for Multi-Label Class-Incremental Learning with CLIP
Kaile Du, Zihan Ye, Junzhou Xie +7
Multi-label class-incremental learning (MLCIL) continuously expands the label space while recognizing multiple co-occurring categories, making catastrophic forgetting a central cha…
CALA: A Class-Aware Logit Adapter for Few-Shot Class-Incremental Learning
Chengyan Liu, Linglan Zhao, Fan Lyu +3
Few-Shot Class-Incremental Learning (FSCIL) defines a practical but challenging task where models are required to continuously learn novel concepts with only a few training samples…
Rebalancing Multi-Label Class-Incremental Learning
Kaile Du, Yifan Zhou, Fan Lyu +5
Multi-label class-incremental learning (MLCIL) is essential for real-world multi-label applications, allowing models to learn new labels while retaining previously learned knowledg…
Confidence Self-Calibration for Multi-Label Class-Incremental Learning
Kaile Du, Yifan Zhou, Fan Lyu +3
The partial label challenge in Multi-Label Class-Incremental Learning (MLCIL) arises when only the new classes are labeled during training, while past and future labels remain unav…