Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Weakly-supervised Salient Instance Detection
Existing salient instance detection (SID) methods typically learn from pixel-level annotated datasets. In this paper, we present the first weakly-supervised approach to the SID problem. Although weak supervision has been…
Boundary DetectionObject LocalizationSaliency DetectionIn-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize the target object using only the image-level supervision. Recent methods encourage the model to activate feature maps over the entire object by dropping the m…
Contrastive LearningObjectObject LocalizationWeakly-Supervised Object Localization3D Object Localization Using 2D Estimates for Computer Vision Applications
A technique for object localization based on pose estimation and camera calibration is presented. The 3-dimensional (3D) coordinates are estimated by collecting multiple 2-dimensional (2D) images of the object and are ut…
Camera CalibrationObjectObject LocalizationPose Estimation+1Polyp-artifact relationship analysis using graph inductive learned representations
The diagnosis process of colorectal cancer mainly focuses on the localization and characterization of abnormal growths in the colon tissue known as polyps. Despite recent advances in deep object localization, the localiz…
Graph Representation LearningObject LocalizationRegion ProposalRepresentation LearningBOP Challenge 2020 on 6D Object Localization
This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object po…
6D Pose Estimation6D Pose Estimation using RGB6D Pose Estimation using RGBDData Augmentation+3Adaptive Label Smoothing
This paper concerns the use of objectness measures to improve the calibration performance of Convolutional Neural Networks (CNNs). CNNs have proven to be very good classifiers and generally localize objects well; however…
General ClassificationObject LocalizationTransfer LearningHow semantic and geometric information mutually reinforce each other in ToF object localization
We propose a novel approach to localize a 3D object from the intensity and depth information images provided by a Time-of-Flight (ToF) sensor. Our method uses two CNNs. The first one uses raw depth and intensity images a…
ObjectObject LocalizationAnchor-free Small-scale Multispectral Pedestrian Detection
Multispectral images consisting of aligned visual-optical (VIS) and thermal infrared (IR) image pairs are well-suited for practical applications like autonomous driving or visual surveillance. Such data can be used to in…
Autonomous DrivingData AugmentationObjectObject Localization+1Sketch-Guided Object Localization in Natural Images
We introduce the novel problem of localizing all the instances of an object (seen or unseen during training) in a natural image via sketch query. We refer to this problem as sketch-guided object localization. This proble…
Image RetrievalObjectobject-detectionObject Detection+4Parts-Based Articulated Object Localization in Clutter Using Belief Propagation
Robots working in human environments must be able to perceive and act on challenging objects with articulations, such as a pile of tools. Articulated objects increase the dimensionality of the pose estimation problem, an…
ObjectObject LocalizationPose EstimationEntropy Guided Adversarial Model for Weakly Supervised Object Localization
Weakly Supervised Object Localization is challenging because of the lack of bounding box annotations. Previous works tend to generate a class activation map i.e CAM to localize the object. Unfortunately, the network acti…
ObjectObject LocalizationWeakly-Supervised Object LocalizationEigen-CAM: Class Activation Map using Principal Components
Deep neural networks are ubiquitous due to the ease of developing models and their influence on other domains. At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representa…
Object LocalizationWeakly-Supervised Object LocalizationRethinking Class Activation Mapping for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) is a task of localizing an object in an image only using image-level labels. To tackle the WSOL problem, most previous studies have followed the conventional class activation …
ObjectObject LocalizationWeakly-Supervised Object LocalizationObject-Aware Centroid Voting for Monocular 3D Object Detection
Monocular 3D object detection aims to detect objects in a 3D physical world from a single camera. However, recent approaches either rely on expensive LiDAR devices, or resort to dense pixel-wise depth estimation that cau…
3D Object DetectionDepth EstimationMonocular 3D Object DetectionObject+3A Generic Visualization Approach for Convolutional Neural Networks
Retrieval networks are essential for searching and indexing. Compared to classification networks, attention visualization for retrieval networks is hardly studied. We formulate attention visualization as a constrained op…
ClassificationGeneral ClassificationObject LocalizationRetrieval+1Geometry Constrained Weakly Supervised Object Localization
We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization (WSOL). GC-Net consists of three modules: a detector, a generator and a classifier. The detector predicts the object loc…
ObjectObject LocalizationWeakly-Supervised Object LocalizationRepPoints V2: Verification Meets Regression for Object Detection
Verification and regression are two general methodologies for prediction in neural networks. Each has its own strengths: verification can be easier to infer accurately, and regression is more efficient and applicable to …
Instance SegmentationObjectobject-detectionObject Detection+4Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as "black-box" and lack of interpretability. One main reason is due to the filter-class entanglement --…
Object LocalizationCross-Modal Weighting Network for RGB-D Salient Object Detection
Depth maps contain geometric clues for assisting Salient Object Detection (SOD). In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channe…
object-detectionObject DetectionObject LocalizationRGB-D Salient Object Detection+2Evaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal WSOL work of class activation mapping (C…
Few-Shot LearningModel SelectionObject LocalizationWeakly-Supervised Object Localization