scholarly journals TCL: Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition

2022 ◽  
pp. 1-1
Author(s):  
Yanqing Shen ◽  
Ruotong Wang ◽  
Weiliang Zuo ◽  
Nanning Zheng
Keyword(s):  
Learning Strategy ◽  
Place Recognition ◽  
Tightly Coupled ◽  
Weakly Supervised

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

scholarly journals AAWS-Net: Anatomy-aware weakly-supervised learning network for breast mass segmentation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256830
Author(s):  
Yeheng Sun ◽  
Yule Ji
Keyword(s):  
Supervised Learning ◽  
Learning Strategy ◽  
Medical Image Analysis ◽  
Training Data ◽  
Breast Mass ◽  
Weakly Supervised Learning ◽  
Learning Network ◽  
Teacher Student ◽  
Mass Segmentation ◽  
Weakly Supervised

Accurate segmentation of breast masses is an essential step in computer aided diagnosis of breast cancer. The scarcity of annotated training data greatly hinders the model’s generalization ability, especially for the deep learning based methods. However, high-quality image-level annotations are time-consuming and cumbersome in medical image analysis scenarios. In addition, a large amount of weak annotations is under-utilized which comprise common anatomy features. To this end, inspired by teacher-student networks, we propose an Anatomy-Aware Weakly-Supervised learning Network (AAWS-Net) for extracting useful information from mammograms with weak annotations for efficient and accurate breast mass segmentation. Specifically, we adopt a weakly-supervised learning strategy in the Teacher to extract anatomy structure from mammograms with weak annotations by reconstructing the original image. Besides, knowledge distillation is used to suggest morphological differences between benign and malignant masses. Moreover, the prior knowledge learned from the Teacher is introduced to the Student in an end-to-end way, which improves the ability of the student network to locate and segment masses. Experiments on CBIS-DDSM have shown that our method yields promising performance compared with state-of-the-art alternative models for breast mass segmentation in terms of segmentation accuracy and IoU.

scholarly journals NetVLAD: CNN Architecture for Weakly Supervised Place Recognition

2018 ◽  
Vol 40 (6) ◽  
pp. 1437-1451 ◽  
Author(s):  
Relja Arandjelovic ◽  
Petr Gronat ◽  
Akihiko Torii ◽  
Tomas Pajdla ◽  
Josef Sivic
Keyword(s):  
Place Recognition ◽  
Weakly Supervised

Weakly Supervised Learning Strategy for Lung Defect Segmentation

2019 ◽  
pp. 541-548
Author(s):  
Robin Sandkühler ◽  
Christoph Jud ◽  
Grzegorz Bauman ◽  
Corin Willers ◽  
Orso Pusterla ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Learning Strategy ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

scholarly journals NetVLAD: CNN Architecture for Weakly Supervised Place Recognition

2016 ◽  
Author(s):  
Relja Arandjelovic ◽  
Petr Gronat ◽  
Akihiko Torii ◽  
Tomas Pajdla ◽  
Josef Sivic
Keyword(s):  
Place Recognition ◽  
Weakly Supervised
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