Semantic Feature Disentanglement versus Attention-Based Relation Modeling for Mitigating Category Bias in Few-Shot Object

Abstract

Abstract: Many meta-learning methods are proposed for few-shot detection. However, previous most methods have two main problems, poor detection APs, and strong bias because of imbalance and insufficient datasets. Previous works mainly alleviate these issues by additional datasets, multi-relation attention mechanisms and sub-modules. However, they require more cost. In this work, for meta-learning, we find that the main challenges focus on related or irrelevant semantic features between categories. Therefore, based on semantic features, we propose a Top-C classification loss (i.e., TCL-C) for classificat

Research Question

Does integrating semantic feature disentanglement into transformer-based few-shot detectors reduce category bias more effectively than attention-based relation modeling on few-shot object detection benchmarks?

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