Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Semantic Segmentation with Boundary Neural Fields
The state-of-the-art in semantic segmentation is currently represented by fully convolutional networks (FCNs). However, FCNs use large receptive fields and many pooling layers, both of which cause blurring and low spatia…
Boundary Detectionglobal-optimizationObject LocalizationSegmentation+1Occlusion-Aware Object Localization, Segmentation and Pose Estimation
We present a learning approach for localization and segmentation of objects in an image in a manner that is robust to partial occlusion. Our algorithm produces a bounding box around the full extent of the object and labe…
3D Pose EstimationObjectObject LocalizationPose Estimation+1Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks
Successful visual object recognition methods typically rely on training datasets containing lots of richly annotated images. Annotating object bounding boxes is both expensive and subjective. We describe a weakly supervi…
General ClassificationObjectObject LocalizationObject Recognition+1Image Retrieval Using Scene Graphs
This paper develops a novel framework for semantic image retrieval based on the notion of a scene graph. Our scene graphs represent objects ("man", "boat"), attributes of objects ("boat is white") and relationships betwe…
Image RetrievalObject LocalizationRetrievalJoint SFM and Detection Cues for Monocular 3D Localization in Road Scenes
We present a system for fast and highly accurate 3D localization of objects like cars in autonomous driving applications, using a single camera. Our localization framework jointly uses information from complementary moda…
Autonomous DrivingMotion SegmentationObjectobject-detection+3Object detection via a multi-region & 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+2Weakly 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+1Deep Joint Task Learning for Generic Object Extraction
This paper investigates how to extract objects-of-interest without relying on hand-craft features and sliding windows approaches, that aims to jointly solve two sub-tasks: (i) rapidly localizing salient objects from imag…
ObjectObject LocalizationSemantic SegmentationObject localization in ImageNet by looking out of the window
We propose a method for annotating the location of objects in ImageNet. Traditionally, this is cast as an image window classification problem, where each window is considered independently and scored based on its appeara…
General ClassificationObject LocalizationObject Detectors Emerge in Deep Scene CNNs
With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state o…
General ClassificationObjectObject LocalizationScene Classification+1GP-select: Accelerating EM using adaptive subspace preselection
We propose a nonparametric procedure to achieve fast inference in generative graphical models when the number of latent states is very large. The approach is based on iterative latent variable preselection, where we alte…
Object LocalizationObject Localization based on Structural SVM using Privileged Information
We propose a structured prediction algorithm for object localization based on Support Vector Machines (SVMs) using privileged information. Privileged information provides useful high-level knowledge for image understandi…
ObjectObject LocalizationStructured PredictionEfficient Object Localization Using Convolutional Networks
Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce comp…
ObjectObject LocalizationPose EstimationPositionZero-Aliasing Correlation Filters for Object Recognition
Correlation filters (CFs) are a class of classifiers that are attractive for object localization and tracking applications. Traditionally, CFs have been designed in the frequency domain using the discrete Fourier transfo…
ObjectObject LocalizationObject RecognitionDensity-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 Localization1-HKUST: Object Detection in ILSVRC 2014
The Imagenet Large Scale Visual Recognition Challenge (ILSVRC) is the one of the most important big data challenges to date. We participated in the object detection track of ILSVRC 2014 and received the fourth place amon…
Objectobject-detectionObject DetectionObject Localization+1Self-taught Object Localization with Deep Networks
This paper introduces self-taught object localization, a novel approach that leverages deep convolutional networks trained for whole-image recognition to localize objects in images without additional human supervision, i…
ClusteringObjectObject LocalizationRobust Scale Estimation in Real-Time Monocular SFM for Autonomous Driving
Scale drift is a crucial challenge for monocular autonomous driving to emulate the performance of stereo. This paper presents a real-time monocular SFM system that corrects for scale drift using a novel cue combination f…
Autonomous DrivingObjectobject-detectionObject Detection+1Multi-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+1Microsoft COCO: Common Objects in Context
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved…
Instance SegmentationObjectObject LocalizationObject Recognition+3