Papers Weakly-Supervised Object Localization
“Weakly-Supervised Object Localization” 태그가 달린 논문 140편 · 필터 해제
Improved Techniques For Weakly-Supervised Object Localization
We propose an improved technique for weakly-supervised object localization. Conventional methods have a limitation that they focus only on most discriminative parts of the target objects. The recent study addressed this …
Data AugmentationObjectObject LocalizationWeakly-Supervised Object LocalizationProgressive Representation Adaptation for Weakly Supervised Object Localization
We address the problem of weakly supervised object localization where only image-level annotations are available for training object detectors. Numerous methods have been proposed to tackle this problem through mining ob…
ClassificationGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Soft Proposal Networks for Weakly Supervised Object Localization
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training. Object proposal is an effective component in localization, but often computation…
ObjectObject LocalizationWeakly Supervised Object DetectionWeakly-Supervised Object LocalizationTwo-Phase Learning for Weakly Supervised Object Localization
Weakly supervised semantic segmentation and localiza- tion have a problem of focusing only on the most important parts of an image since they use only image-level annota- tions. In this paper, we solve this problem funda…
ObjectObject LocalizationSegmentationSemantic Segmentation+5Object-Extent Pooling for Weakly Supervised Single-Shot Localization
In the face of scarcity in detailed training annotations, the ability to perform object localization tasks in real-time with weak-supervision is very valuable. However, the computational cost of generating and evaluating…
ObjectObject LocalizationRegion ProposalWeakly-Supervised Object LocalizationWILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features. Our model is trained using only global image lab…
General Classificationimage-classificationImage ClassificationObject Localization+4How hard can it be? Estimating the difficulty of visual search in an image
We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the PASCAL VOC 2012 data set through a crowd…
Object LocalizationWeakly-Supervised Object LocalizationTraining object class detectors with click supervision
Training object class detectors typically requires a large set of images with objects annotated by bounding boxes. However, manually drawing bounding boxes is very time consuming. In this paper we greatly reduce annotati…
Multiple Instance LearningObjectObject LocalizationWeakly-Supervised Object LocalizationDeep Self-Taught Learning for Weakly Supervised Object Localization
Most existing weakly supervised localization (WSL) approaches learn detectors by finding positive bounding boxes based on features learned with image-level supervision. However, those features do not contain spatial loca…
ObjectObject LocalizationWeakly Supervised Object DetectionWeakly-Supervised Object LocalizationHide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization
We propose `Hide-and-Seek', a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminativ…
Action LocalizationObjectObject LocalizationWeakly Supervised Action Localization+1Weakly Supervised Object Localization Using Things and Stuff Transfer
We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations. The source and t…
Multiple Instance LearningObjectObject LocalizationSegmentation+1ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization
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 th…
ObjectObject LocalizationObject RecognitionWeakly Supervised Object Detection+1Weakly Supervised Object Localization Using Size Estimates
We present a technique for weakly supervised object localization (WSOL), building on the observation that WSOL algorithms usually work better on images with bigger objects. Instead of training the object detector on the …
ObjectObject LocalizationWeakly-Supervised Object LocalizationWeakly Supervised Object Localization With Progressive Domain Adaptation
We address the problem of weakly supervised object localization where only image-level annotations are available for training. Many existing approaches tackle this problem through object proposal mining. However, a subst…
ClassificationDomain AdaptationGeneral ClassificationMulti-Label Classification+5Improving Weakly-Supervised Object Localization By Micro-Annotation
Weakly-supervised object localization methods tend to fail for object classes that consistently co-occur with the same background elements, e.g. trains on tracks. We propose a method to overcome these failures by adding …
ObjectObject LocalizationSemantic SegmentationWeakly-Supervised Object LocalizationSelf-Transfer Learning for Fully Weakly Supervised Object Localization
Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches…
Medical Image AnalysisObjectObject LocalizationTransfer Learning+2Learning Deep Features for Discriminative Localization
In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on im…
Object LocalizationWeakly-Supervised Object LocalizationWeakly Supervised Object Localization with Multi-fold Multiple Instance Learning
Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakl…
Multiple Instance LearningObjectObject LocalizationWeakly-supervised Learning+1Density-Based Region Search with Arbitrary Shape for Object Localization
Region search is widely used for object localization. Typically, the region search methods project the score of a classifier into an image plane, and then search the region with the maximal score. The recently proposed r…
Object LocalizationWeakly-Supervised Object LocalizationMulti-fold MIL Training for Weakly Supervised Object Localization
Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakl…
Multiple Instance LearningObjectObject LocalizationWeakly-supervised Learning+1