30 citations · 49 across the 8 of their papers we have counts for
4 papers · 1 filter
A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval
Qi Zhao, Xu Wang, Shuchang Lyu +2
Large-scale fine-grained image retrieval has two main problems. First, low dimensional feature embedding can fasten the retrieval process but bring accuracy reduce due to overlooki…
A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation
Qi Zhao, Binghao Liu, Shuchang Lyu +1
Few-shot segmentation focuses on the generalization of models to segment unseen object with limited annotated samples. However, existing approaches still face two main challenges.…
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation
Binghao Liu, Yao Ding, Jianbin Jiao +2
Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with…
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…