Bias-Eliminated Semantic Refinement for Any-Shot Learning.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Semantic embedding aids visual feature generation for unseen objects when training data is limited.
• Existing methods suffer from weak consistency between semantic descriptions and visual features due to external annotation paradigms.

Purpose of the Study:

• To refine coarse-grained semantic descriptions for any-shot learning (zero-shot, generalized zero-shot, few-shot learning).
• To develop a model that generates bias-eliminated visual features for disjoint classes.

Main Methods:

• Introduced the semantic refinement Wasserstein generative adversarial network (SRWGAN) model.
• Employed multi-head representation and hierarchical alignment techniques for semantic refinement.
• Designed for bias-eliminated condition identification for feature generation in both inductive and transductive settings.

Main Results:

• Achieved state-of-the-art performance on six benchmark datasets for any-shot learning.
• Obtained 70.2% harmonic accuracy on Caltech UCSD Birds (CUB) and 82.2% on Oxford Flowers (FLO) in generalized zero-shot learning.
• Demonstrated bias-eliminated generation capabilities through various visualizations.

Conclusions:

• The SRWGAN model effectively refines semantic descriptions for improved visual feature generation in any-shot learning.
• The proposed techniques enable robust knowledge transfer and accurate feature generation for unseen objects.
• The model's performance indicates a significant advancement in addressing the challenges of limited training data in computer vision tasks.