Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization

arXiv:2403.10298v1 Announce Type: new
Abstract: Exploring and mining subtle yet distinctive features between sub-categories with similar appearances is crucial for fine-grained visual categorization (FGVC). However, less effort has been devoted to assessing the quality of extracted visual representations. Intuitively, the network may struggle to capture discriminative features from low-quality samples, which leads to a significant decline in FGVC performance. To tackle this challenge, we propose a weakly supervised Context-Semantic Quality Awareness Network (CSQA-Net) for FGVC. In this network, to model …

abstract arxiv context cs.cv features fine-grained however leads low mining network quality samples semantic struggle type visual