Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization

2021 ◽  
pp. 1-1
Author(s):  
Bo Wang ◽  
Chunfeng Yuan ◽  
Bing Li ◽  
Xinmiao Ding ◽  
Zeya Lia ◽  
...  
Keyword(s):  
Object Localization ◽  
Multi Scale ◽  
Discriminative Feature ◽  
Weakly Supervised

scholarly journals A Two-Branch Network for Weakly Supervised Object Localization

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 955
Author(s):  
Chang Sun ◽  
Yibo Ai ◽  
Sheng Wang ◽  
Weidong Zhang
Keyword(s):  
Ground Truth ◽  
Object Localization ◽  
Feature Extracting ◽  
Multi Scale ◽  
Main Challenge ◽  
Branch Model ◽  
Localization Precision ◽  
Object Parts ◽  
Weakly Supervised ◽  
Scale Detection

Weakly supervised object localization (WSOL) has attracted intense interest in computer vision for instance level annotations. As a hot research topic, a number of existing works concentrated on utilizing convolutional neural network (CNN)-based methods, which are powerful in extracting and representing features. The main challenge in CNN-based WSOL methods is to obtain features covering the entire target objects, not only the most discriminative object parts. To overcome this challenge and to improve the detection performance of feature extracting related WSOL methods, a CNN-based two-branch model was presented in this paper to locate objects using supervised learning. Our method contained two branches, including a detection branch and a self-attention branch. During the training process, the two branches interacted with each other by regarding the segmentation mask from the other branch as the pseudo ground truth labels of itself. Our model was able to focus on capturing the information of all the object parts due to the self-attention mechanism. Additionally, we embedded multi-scale detection into our two-branch method to output two-scale features. We evaluated our two-branch network on the CUB-200-2011 and VOC2007 datasets. The pointing localization, intersection over union (IoU) localization, and correct localization precision (CorLoc) results demonstrated competitive performance with other state-of-the-art methods in WSOL.

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 Fine-grained classification based on multi-scale pyramid convolution networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254054
Author(s):  
Gaihua Wang ◽  
Lei Cheng ◽  
Jinheng Lin ◽  
Yingying Dai ◽  
Tianlun Zhang
Keyword(s):  
Data Augmentation ◽  
Convolution Kernel ◽  
Residual Network ◽  
Complementary Information ◽  
Fine Grained ◽  
Multi Scale ◽  
Key Factor ◽  
Object Part ◽  
Weakly Supervised ◽  
Subtle Feature

The large intra-class variance and small inter-class variance are the key factor affecting fine-grained image classification. Recently, some algorithms have been more accurate and efficient. However, these methods ignore the multi-scale information of the network, resulting in insufficient ability to capture subtle changes. To solve this problem, a weakly supervised fine-grained classification network based on multi-scale pyramid is proposed in this paper. It uses pyramid convolution kernel to replace ordinary convolution kernel in residual network, which can expand the receptive field of the convolution kernel and use complementary information of different scales. Meanwhile, the weakly supervised data augmentation network (WS-DAN) is used to prevent over fitting and improve the performance of the model. In addition, a new attention module, which includes spatial attention and channel attention, is introduced to pay more attention to the object part in the image. The comprehensive experiments are carried out on three public benchmarks. It shows that the proposed method can extract subtle feature and achieve classification effectively.

Multi-Scale Structure-Aware Network for Weakly Supervised Temporal Action Detection

2021 ◽  
pp. 1-1
Author(s):  
Wenfei Yang ◽  
Tianzhu Zhang ◽  
Zhendong Mao ◽  
Yongdong Zhanga ◽  
Qi Tian ◽  
...  
Keyword(s):  
Scale Structure ◽  
Action Detection ◽  
Multi Scale ◽  
Weakly Supervised ◽  
Temporal Action
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