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ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
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TLDR
This work introduces two types of context-aware guidance models, additive and contrastive models, that leverage their surrounding context regions to improve localization in objects in images using image-level supervision only.Abstract:
We aim to localize objects in images using image-level supervision only. Previous approaches to this problem mainly focus on discriminative object regions and often fail to locate precise object boundaries. We address this problem by introducing two types of context-aware guidance models, additive and contrastive models, that leverage their surrounding context regions to improve localization. The additive model encourages the predicted object region to be supported by its surrounding context region. The contrastive model encourages the predicted object region to be outstanding from its surrounding context region. Our approach benefits from the recent success of convolutional neural networks for object recognition and extends Fast R-CNN to weakly supervised object localization. Extensive experimental evaluation on the PASCAL VOC 2007 and 2012 benchmarks shows hat our context-aware approach significantly improves weakly supervised localization and detection.read more
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Proceedings ArticleDOI
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization
Krishna Kumar Singh,Yong Jae Lee +1 more
TL;DR: The key idea is to hide patches in a training image randomly, forcing the network to seek other relevant parts when the most discriminative part is hidden, which obtains superior performance compared to previous methods for weakly-supervised object localization on the ILSVRC dataset.
Proceedings ArticleDOI
Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation
TL;DR: In this paper, a cross-domain weakly supervised object detection framework is proposed to detect common objects in a variety of image domains without instance-level annotations, where the classes to be detected in the target domain are all or a subset of those in the source domain.
Journal ArticleDOI
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
TL;DR: This paper proposes an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire, which uses smaller convolutional kernels and contains no dense, fully connected layers.
Journal ArticleDOI
Early fire detection using convolutional neural networks during surveillance for effective disaster management
TL;DR: An early fire detection framework using fine-tuned convolutional neural networks for CCTV surveillance cameras, which can detect fire in varying indoor and outdoor environments is proposed and an adaptive prioritization mechanism for cameras in the surveillance system is proposed to ensure the autonomous response.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Journal ArticleDOI
The Pascal Visual Object Classes (VOC) Challenge
TL;DR: The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse.
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Fast R-CNN
TL;DR: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection that builds on previous work to efficiently classify object proposals using deep convolutional networks.
Journal ArticleDOI
Object Detection with Discriminatively Trained Part-Based Models
TL;DR: An object detection system based on mixtures of multiscale deformable part models that is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges is described.