S Huang, X Wang, D Tao - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Learning mid-level representation for fine-grained recognition is easily dominated by a limited number of highly discriminative patterns, degrading its robustness and generalization …
We aim to divide the problem space of fine-grained recognition into some specific regions. To achieve this, we develop a unified framework based on a mixture of experts. Due to …
Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of …
Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences …
L Zhu, T Chen, J Yin, S See… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Extracting and using class-discriminative features is critical for fine-grained recognition. Existing works have demonstrated the possibility of applying deep CNNs to exploit features …
Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses. A key to address this problem is to localize …
Fine-grained recognition distinguishes among categories with subtle visual differences. In order to differentiate between these challenging visual categories, it is helpful to leverage …
Y Zhao, K Yan, F Huang, J Li - Proceedings of the IEEE/CVF …, 2021 - openaccess.thecvf.com
Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects. Most of the existing works tend to amplify …
P Zhuang, Y Wang, Y Qiao - Proceedings of the AAAI conference on …, 2020 - ojs.aaai.org
Fine-grained classification is a challenging problem, due to subtle differences among highly- confused categories. Most approaches address this difficulty by learning discriminative …