5 citations · 5 across the 5 of their papers we have counts for
7 papers
Neural Dependencies Emerging from Learning Massive Categories
Ruili Feng, Kecheng Zheng, Kai Zhu +7
This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category c…
FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization
Kecheng Zheng, Yang Cao, Kai Zhu +2
MLP-like models built entirely upon multi-layer perceptrons have recently been revisited, exhibiting the comparable performance with transformers. It is one of most promising archi…
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
Kai Zhu, Wei Zhai, Yang Cao +2
Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimizat…
Self-Paced Imbalance Rectification for Class Incremental Learning
Zhiheng Liu, Kai Zhu, Yang Cao
Exemplar-based class-incremental learning is to recognize new classes while not forgetting old ones, whose samples can only be saved in limited memory. The ratio fluctuation of new…
Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
Kai Zhu, Yang Cao, Wei Zhai +2
Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and…
Self-Supervised Tuning for Few-Shot Segmentation
Kai Zhu, Wei Zhai, Zheng-Jun Zha +1
Few-shot segmentation aims at assigning a category label to each image pixel with few annotated samples. It is a challenging task since the dense prediction can only be achieved un…