Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
One Size Doesn't Fit All: Adaptive Label Smoothing
This paper concerns the use of objectness measures to improve the calibration performance of Convolutional Neural Networks (CNNs). Objectness is a measure of likelihood of an object from \textit{any} class being present …
AllObjectObject LocalizationForeground Activation Maps for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels, which has better scalability and practicability than fully supervised methods in the actual deployment. However, wit…
ClassificationObjectObject LocalizationWeakly-Supervised Object LocalizationGridMix: Strong regularization through local context mapping
Recently developed regularization techniques improve the networks generalization by only considering the global context. Therefore, the network tends to focus on a few most discriminative subregions of an image for predi…
Adversarial RobustnessData AugmentationObject LocalizationWeakly-Supervised Object LocalizationOne-Shot Object Localization Using Learnt Visual Cues via Siamese Networks
A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects. In this work, a visual cue is used to specify a novel object of interest which must be local…
ObjectObject LocalizationEDN: Salient Object Detection via Extremely-Downsampled Network
Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and discovering fine details, respectively.…
DecoderObjectobject-detectionObject Detection+2Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images
Deep learning networks have shown promising performance for accurate object localization in medial images, but require large amount of annotated data for supervised training, which is expensive and expertise burdensome. …
Contrastive LearningObject LocalizationPositionPosition regression+2R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection
Current neural networks-based object detection approaches processing LiDAR point clouds are generally trained from one kind of LiDAR sensors. However, their performances decrease when they are tested with data coming fro…
3D Object DetectionData AugmentationObjectobject-detection+3MOLTR: Multiple Object Localisation, Tracking, and Reconstruction from Monocular RGB Videos
Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric ma…
BenchmarkingObjectObject LocalizationCIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud
Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well …
3D Object DetectionBirds Eye View Object DetectionClassificationGeneral Classification+1Video Anomaly Detection by Estimating Likelihood of Representations
Video anomaly detection is a challenging task not only because it involves solving many sub-tasks such as motion representation, object localization and action recognition, but also because it is commonly considered as a…
Action RecognitionAnomaly DetectionClusteringDensity Estimation+2RfD-Net: Point Scene Understanding by Semantic Instance Reconstruction
Semantic scene understanding from point clouds is particularly challenging as the points reflect only a sparse set of the underlying 3D geometry. Previous works often convert point cloud into regular grids (e.g. voxels o…
3D geometryObjectobject-detectionObject Detection+3Hierarchical Complementary Learning for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) is a challenging problem which aims to localize objects with only image-level labels. Due to the lack of ground truth bounding boxes, class labels are mainly employed to train…
ObjectObject LocalizationWeakly-Supervised Object LocalizationMulti-view Sensor Fusion by Integrating Model-based Estimation and Graph Learning for Collaborative Object Localization
Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles. To ena…
Autonomous DrivingGraph LearningObjectObject Localization+2BirdSLAM: Monocular Multibody SLAM in Bird's-Eye View
In this paper, we present BirdSLAM, a novel simultaneous localization and mapping (SLAM) system for the challenging scenario of autonomous driving platforms equipped with only a monocular camera. BirdSLAM tackles challen…
Autonomous DrivingMonocular ReconstructionObject LocalizationSimultaneous Localization and MappingTTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency Loss
Semi-supervised video object segmentation (semi-VOS) is widely used in many applications. This task is tracking class-agnostic objects from a given target mask. For doing this, various approaches have been developed base…
ObjectObject LocalizationOptical Flow EstimationSemantic Segmentation+4Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images
In synchrotron-based Computed Tomography (CT) there is a trade-off between spatial resolution, field of view and speed of positioning and alignment of samples. The problem is even more prominent for high-throughput tomog…
Computed Tomography (CT)Object LocalizationSelf-Supervised LearningSemantic SegmentationFaraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion
Learned pointcloud representations do not generalize well with an increase in distance to the sensor. For example, at a range greater than 60 meters, the sparsity of lidar pointclouds reaches to a point where even humans…
3D Object DetectionObjectobject-detectionObject Detection+1LID 2020: The Learning from Imperfect Data Challenge Results
Learning from imperfect data becomes an issue in many industrial applications after the research community has made profound progress in supervised learning from perfectly annotated datasets. The purpose of the Learning …
object-detectionObject DetectionObject LocalizationScene Parsing+5Zoom-CAM: Generating Fine-grained Pixel Annotations from Image Labels
Current weakly supervised object localization and segmentation rely on class-discriminative visualization techniques to generate pseudo-labels for pixel-level training. Such visualization methods, including class activat…
Object LocalizationSegmentationSemantic SegmentationWeakly-Supervised Object Localization+2Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a two-stage learning framework to perform…
ObjectObject Localization