26 citations · 30 across the 3 of their papers we have counts for
4 papers · 1 filter
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning
Binghao Liu, Boyu Yang, Lingxi Xie +3
Few-shot class-incremental learning (FSCIL) faces challenges of memorizing old class distributions and estimating new class distributions given few training samples. In this study,…
Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning
Boyu Yang, Mingbao Lin, Binghao Liu +4
Few-shot class-incremental learning (FSCIL), which targets at continuously expanding model's representation capacity under few supervisions, is an important yet challenging problem…
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
Bohao Li, Boyu Yang, Chang Liu +3
Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects. However,an imp…
Prototype Mixture Models for Few-shot Semantic Segmentation
Boyu Yang, Chang Liu, Bohao Li +2
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. Using a single prototype acquired direct…