Few-Shot Inspired Generative Zero-Shot Learning

• URL: http://arxiv.org/abs/2507.01026v1
• Date: Wed, 18 Jun 2025 02:39:36 GMT
• Title: Few-Shot Inspired Generative Zero-Shot Learning
• Authors: Md Shakil Ahamed Shohag, Q. M. Jonathan Wu, Farhad Pourpanah,
• Abstract summary: Generative zero-shot learning (ZSL) methods typically synthesize visual features for unseen classes.<n>We propose FSIGenZ, a few-shot-inspired generative ZSL framework that reduces reliance on large-scale feature synthesis.<n>Experiments on SUN, AwA2, and CUB benchmarks demonstrate that FSIGenZ achieves competitive performance using far fewer synthetic features.
• Score: 14.66239393852298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative zero-shot learning (ZSL) methods typically synthesize visual features for unseen classes using predefined semantic attributes, followed by training a fully supervised classification model. While effective, these methods require substantial computational resources and extensive synthetic data, thereby relaxing the original ZSL assumptions. In this paper, we propose FSIGenZ, a few-shot-inspired generative ZSL framework that reduces reliance on large-scale feature synthesis. Our key insight is that class-level attributes exhibit instance-level variability, i.e., some attributes may be absent or partially visible, yet conventional ZSL methods treat them as uniformly present. To address this, we introduce Model-Specific Attribute Scoring (MSAS), which dynamically re-scores class attributes based on model-specific optimization to approximate instance-level variability without access to unseen data. We further estimate group-level prototypes as clusters of instances based on MSAS-adjusted attribute scores, which serve as representative synthetic features for each unseen class. To mitigate the resulting data imbalance, we introduce a Dual-Purpose Semantic Regularization (DPSR) strategy while training a semantic-aware contrastive classifier (SCC) using these prototypes. Experiments on SUN, AwA2, and CUB benchmarks demonstrate that FSIGenZ achieves competitive performance using far fewer synthetic features.

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