7 citations · 12 across the 26 of their papers we have counts for
9 papers · 1 filter
Reconstruction Target Matters in Masked Image Modeling for Cross-Domain Few-Shot Learning
Ran Ma, Yixiong Zou, Yuhua Li +1
Cross-Domain Few-Shot Learning (CDFSL) requires the model to transfer knowledge from the data-abundant source domain to data-scarce target domains for fast adaptation, where the la…
Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation
Jintao Tong, Yixiong Zou, Yuhua Li +1
Cross-domain few-shot segmentation (CD-FSS) is proposed to first pre-train the model on a large-scale source-domain dataset, and then transfer the model to data-scarce target-domai…
MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +3
Humans exhibit a remarkable ability to learn quickly from a limited number of labeled samples, a capability that starkly contrasts with that of current machine learning systems. Un…
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition
Zhenyu Zhang, Guangyao Chen, Yixiong Zou +2
Few-shot open-set recognition (FSOR) is a challenging task that requires a model to recognize known classes and identify unknown classes with limited labeled data. Existing approac…
Compositional Few-Shot Class-Incremental Learning
Yixiong Zou, Shanghang Zhang, Haichen Zhou +2
Few-shot class-incremental learning (FSCIL) is proposed to continually learn from novel classes with only a few samples after the (pre-)training on base classes with sufficient dat…
Delve into Base-Novel Confusion: Redundancy Exploration for Few-Shot Class-Incremental Learning
Haichen Zhou, Yixiong Zou, Ruixuan Li +2
Few-shot class-incremental learning (FSCIL) aims to acquire knowledge from novel classes with limited samples while retaining information about base classes. Existing methods addre…