Abstract
Current fine-grained visual classification (FGVC) models are isolated. In practice, we first need to identify the coarse-grained label of an object, then select the corresponding FGVC model for recognition. This hinders the application of FGVC algorithms in real-life scenarios. In this paper, we propose an erudite FGVC model jointly trained by several different datasets1 , which can efficiently and accurately predict an object’s fine-grained label across the combined label space. We found through a pilot study that positive and negative transfers co-occur when different datasets are mixed for training, i.e., the knowledge from other datasets is not always useful. Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets. In detail, we reduce negative transfer by decoupling the deep features through many dataset-specific feature extractors. Subsequently, these are channel-wise re-fused to facilitate positive transfer. Finally, we propose a meta-learning based dataset-agnostic spatial attention layer to take full advantage of the multi-dataset training data, given that localisation is dataset-agnostic between different datasets. Experimental results across 11 different mixed-datasets built on four different FGVC datasets demonstrate the effectiveness of the proposed method. Furthermore, the proposed method can be easily combined with existing FGVC methods to obtain state-of-the-art results. Our code is available at https://github.com/PRIS-CV/An-EruditeFGVC-Model
Original language | English |
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Title of host publication | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 7268-7277 |
Number of pages | 10 |
ISBN (Electronic) | 9798350301298 |
ISBN (Print) | 9798350301304 |
DOIs | |
Publication status | Published - 22 Aug 2023 |
Event | The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 - Vancouver Convention Center, Vancouver, Canada Duration: 18 Jun 2023 → 22 Jun 2023 https://cvpr2023.thecvf.com/ |
Publication series
Name | IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
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Publisher | IEEE |
ISSN (Print) | 1063-6919 |
ISSN (Electronic) | 2575-7075 |
Conference
Conference | The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 |
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Abbreviated title | CVPR 2023 |
Country/Territory | Canada |
City | Vancouver |
Period | 18/06/23 → 22/06/23 |
Internet address |