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Weakly supervised fine-grained recognition based on spatial-channel aware attention filters
- Source :
- Multimedia Tools and Applications. 80:14409-14427
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Fine-grained recognition is a very challenging issue, since it is difficulty to mine discriminative and subtle feature for objects with similar visual appearance. Because massive manual annotations (e.g., bounding box for discriminative regions) are time-consuming and labor-consuming, existing methods designed single form of attention model outputted discriminative regions in a weakly supervised way. In this paper, we proposed a novel method named a Spatial-Channel Aware Attention Filters (SCAF) to address the weakly supervised fine-grained recognition problem. Compared with the previous attention models, SCAF can obtain attentions-aware features from two dimensions, i.e., the spatial location of image and the channel of feature maps. With the proposed SCAF, the model can enhance the discriminative regions on both spatial and channel dimensions simultaneously. In addition, the multi-channel network multi-level structure are designed to extract richer regional features. Moreover, focal loss is introduced to balance the samples’ distribution of fine-grained image dataset. Comprehensive and comparable experiments are conducted in publicly available datasets, and the experimental results show that our method can achieve the state-of-the-art performance on fine-grained recognition tasks. For instance, we achieve 99.370%, 80.749% accuracy on two underwater datasets respectively, i.e., Fish4Knowlege and Wild Fish.
- Subjects :
- Structure (mathematical logic)
Channel (digital image)
Computer Networks and Communications
Computer science
business.industry
020207 software engineering
Pattern recognition
02 engineering and technology
Image (mathematics)
Discriminative model
Hardware and Architecture
Minimum bounding box
0202 electrical engineering, electronic engineering, information engineering
Media Technology
Feature (machine learning)
Artificial intelligence
business
Software
Subjects
Details
- ISSN :
- 15737721 and 13807501
- Volume :
- 80
- Database :
- OpenAIRE
- Journal :
- Multimedia Tools and Applications
- Accession number :
- edsair.doi...........9ce1b2722d199865ba4d60017dc311fa