Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Seeing with Humans: Gaze-Assisted Neural Image Captioning
Gaze reflects how humans process visual scenes and is therefore increasingly used in computer vision systems. Previous works demonstrated the potential of gaze for object-centric tasks, such as object localization and re…
Image CaptioningObjectObject LocalizationScene Recognition+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 LocalizationActive Object Localization in Visual Situations
We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting fo…
Active Object LocalizationObjectObject LocalizationSlack and Margin Rescaling as Convex Extensions of Supermodular Functions
Slack and margin rescaling are variants of the structured output SVM, which is frequently applied to problems in computer vision such as image segmentation, object localization, and learning parts based object models. Th…
Image SegmentationObject LocalizationSemantic SegmentationStructured PredictionLatent Constrained Correlation Filters for Object Localization
There is a neglected fact in the traditional machine learning methods that the data sampling can actually lead to the solution sampling. We consider this observation to be important because having the solution sampling a…
ObjectObject 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+5Dense CNN Learning with Equivalent Mappings
Large receptive field and dense prediction are both important for achieving high accuracy in pixel labeling tasks such as semantic segmentation. These two properties, however, contradict with each other. A pooling layer …
Age EstimationImage CategorizationObject LocalizationSemantic SegmentationImproving 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 LocalizationDeformable Parts Correlation Filters for Robust Visual Tracking
Deformable parts models show a great potential in tracking by principally addressing non-rigid object deformations and self occlusions, but according to recent benchmarks, they often lag behind the holistic approaches. T…
ObjectObject LocalizationVisual TrackingSimultaneous Food Localization and Recognition
The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With …
NutritionObject LocalizationFast Object Localization Using a CNN Feature Map Based Multi-Scale Search
Object localization is an important task in computer vision but requires a large amount of computational power due mainly to an exhaustive multiscale search on the input image. In this paper, we describe a near real-time…
Object LocalizationObject Boundary Guided Semantic Segmentation
Semantic segmentation is critical to image content understanding and object localization. Recent development in fully-convolutional neural network (FCN) has enabled accurate pixel-level labeling. One issue in previous wo…
ObjectObject LocalizationSegmentationSemantic SegmentationWeakly Supervised Localization using Deep Feature Maps
Object localization is an important computer vision problem with a variety of applications. The lack of large scale object-level annotations and the relative abundance of image-level labels makes a compelling case for we…
General ClassificationObjectObject LocalizationObject Recognition+1Self-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 LocalizationObject Detection via a Multi-Region and Semantic Segmentation-Aware CNN Model
We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing…
Objectobject-detectionObject DetectionObject Localization+1Square Localization for Efficient and Accurate Object Detection
The key contribution of this paper is the compact square object localization, which relaxes the exhaustive sliding window from testing all windows of different combinations of aspect ratios. Square object localization is…
Objectobject-detectionObject DetectionObject LocalizationLocNet: Improving Localization Accuracy for Object Detection
We propose a novel object localization methodology with the purpose of boosting the localization accuracy of state-of-the-art object detection systems. Our model, given a search region, aims at returning the bounding box…
Objectobject-detectionObject DetectionObject LocalizationActive Object Localization with Deep Reinforcement Learning
We present an active detection model for localizing objects in scenes. The model is class-specific and allows an agent to focus attention on candidate regions for identifying the correct location of a target object. This…
Active Object LocalizationDeep Reinforcement LearningObjectObject Localization+3Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform
Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems. Recent work has shown that complementing CNNs with fully-connected conditional random fields (CRFs) can sign…
Edge DetectionImage SegmentationObject LocalizationSegmentation+1