scholarly journals Graph-Propagation Based Correlation Learning for Weakly Supervised Fine-Grained Image Classification

2020 ◽  
Vol 34 (07) ◽  
pp. 12289-12296 ◽  
Author(s):  
Zhuhui Wang ◽  
Shijie Wang ◽  
Haojie Li ◽  
Zhi Dou ◽  
Jianjun Li
Keyword(s):  
Image Classification ◽  
Feature Representation ◽  
Spatial Context ◽  
Discriminative Power ◽  
Fine Grained ◽  
Semantic Correlation ◽  
Discriminative Feature ◽  
Correlation Learning ◽  
Weakly Supervised ◽  
Graph Propagation

The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to pick out the discriminative regions and learn the discriminative features from them. However, most recent WFGIC methods pick out the discriminative regions independently and utilize their features directly, while neglecting the facts that regions' features are mutually semantic correlated and region groups can be more discriminative. To address these issues, we propose an end-to-end Graph-propagation based Correlation Learning (GCL) model to fully mine and exploit the discriminative potentials of region correlations for WFGIC. Specifically, in discriminative region localization phase, a Criss-cross Graph Propagation (CGP) sub-network is proposed to learn region correlations, which establishes correlation between regions and then enhances each region by weighted aggregating other regions in a criss-cross way. By this means each region's representation encodes the global image-level context and local spatial context simultaneously, thus the network is guided to implicitly discover the more powerful discriminative region groups for WFGIC. In discriminative feature representation phase, the Correlation Feature Strengthening (CFS) sub-network is proposed to explore the internal semantic correlation among discriminative patches' feature vectors, to improve their discriminative power by iteratively enhancing informative elements while suppressing the useless ones. Extensive experiments demonstrate the effectiveness of proposed CGP and CFS sub-networks, and show that the GCL model achieves better performance both in accuracy and efficiency.

scholarly journals Centralized Ranking Loss with Weakly Supervised Localization for Fine-Grained Object Retrieval

2018 ◽  
Author(s):  
Xiawu Zheng ◽  
Rongrong Ji ◽  
Xiaoshuai Sun ◽  
Yongjian Wu ◽  
Feiyue Huang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Feature Learning ◽  
Target Object ◽  
Object Retrieval ◽  
Unified Framework ◽  
Fine Grained ◽  
Discriminative Feature ◽  
Triplet Loss ◽  
Weakly Supervised ◽  
Ranking Loss

Fine-grained object retrieval has attracted extensive research focus recently. Its state-of-the-art schemesare typically based upon convolutional neural network (CNN) features. Despite the extensive progress, two issues remain open. On one hand, the deep features are coarsely extracted at image level rather than precisely at object level, which are interrupted by background clutters. On the other hand, training CNN features with a standard triplet loss is time consuming and incapable to learn discriminative features. In this paper, we present a novel fine-grained object retrieval scheme that conquers these issues in a unified framework. Firstly, we introduce a novel centralized ranking loss (CRL), which achieves a very efficient (1,000times training speedup comparing to the triplet loss) and discriminative feature learning by a ?centralized? global pooling. Secondly, a weakly supervised attractive feature extraction is proposed, which segments object contours with top-down saliency. Consequently, the contours are integrated into the CNN response map to precisely extract features ?within? the target object. Interestingly, we have discovered that the combination of CRL and weakly supervised learning can reinforce each other. We evaluate the performance ofthe proposed scheme on widely-used benchmarks including CUB200-2011 and CARS196. We havereported significant gains over the state-of-the-art schemes, e.g., 5.4% over SCDA [Wei et al., 2017]on CARS196, and 3.7% on CUB200-2011.  

scholarly journals Weakly Supervised Fine-Grained Image Classification via Salient Region Localization and Different Layer Feature Fusion

2020 ◽  
Vol 10 (13) ◽  
pp. 4652
Author(s):  
Fangxiong Chen ◽  
Guoheng Huang ◽  
Jiaying Lan ◽  
Yanhui Wu ◽  
Chi-Man Pun ◽  
...  
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Classification Performance ◽  
Training Data ◽  
Classification Model ◽  
Global Features ◽  
Salient Region ◽  
Fine Grained ◽  
Proposed Model ◽  
Weakly Supervised

The fine-grained image classification task is about differentiating between different object classes. The difficulties of the task are large intra-class variance and small inter-class variance. For this reason, improving models’ accuracies on the task heavily relies on discriminative parts’ annotations and regional parts’ annotations. Such delicate annotations’ dependency causes the restriction on models’ practicability. To tackle this issue, a saliency module based on a weakly supervised fine-grained image classification model is proposed by this article. Through our salient region localization module, the proposed model can localize essential regional parts with the use of saliency maps, while only image class annotations are provided. Besides, the bilinear attention module can improve the performance on feature extraction by using higher- and lower-level layers of the network to fuse regional features with global features. With the application of the bilinear attention architecture, we propose the different layer feature fusion module to improve the expression ability of model features. We tested and verified our model on public datasets released specifically for fine-grained image classification. The results of our test show that our proposed model can achieve close to state-of-the-art classification performance on various datasets, while only the least training data are provided. Such a result indicates that the practicality of our model is incredibly improved since fine-grained image datasets are expensive.

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