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cs.CV2026
UniTriGen: Unified Triplet Generation of Aligned Visible-Infrared-Label for Few-Shot RGB-T Semantic Segmentation
Ping Zhou, Haoyu Wang, Mengmeng Zheng +4
RGB-T semantic segmentation requires strictly aligned VIS-IR-Label triplets; however, such aligned triplet data are often scarce in real-world scenarios. Existing generative augmen…
cs.CV2024★ 1 cited
Meta-Exploiting Frequency Prior for Cross-Domain Few-Shot Learning
Fei Zhou, Peng Wang, Lei Zhang +5
Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in…
cs.CV2020
Meta-Generating Deep Attentive Metric for Few-shot Classification
Lei Zhang, Fei Zhou, Wei Wei +1
Learning to generate a task-aware base learner proves a promising direction to deal with few-shot learning (FSL) problem. Existing methods mainly focus on generating an embedding m…