Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review
This paper presents a comprehensive survey on vision-based robotic grasping. We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation. I…
6D Pose Estimation6D Pose Estimation using RGBInstance SegmentationMotion Planning+9CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Regional dropout strategies have been proposed to enhance the performance of convolutional neural network classifiers. They have proved to be effective for guiding the model to attend on less discriminative parts of obje…
Domain GeneralizationImage CaptioningImage ClassificationObject Localization+3Parity Models: A General Framework for Coding-Based Resilience in ML Inference
Machine learning models are becoming the primary workhorses for many applications. Production services deploy models through prediction serving systems that take in queries and return predictions by performing inference …
BIG-bench Machine Learningimage-classificationImage ClassificationObject Localization+3FAST OBJECT LOCALIZATION VIA SENSITIVITY ANALYSIS
Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data. Methods for objec…
General Classificationimage-classificationImage ClassificationObject+3Tactile Mapping and Localization from High-Resolution Tactile Imprints
This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim. The main contributions are the recovery of local shapes from contact, an approach to reconstruct…
ObjectObject LocalizationVocal Bursts Intensity PredictionCompression and Localization in Reinforcement Learning for ATARI Games
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed. While compressing deep neural networks has of late assumed great …
Atari GamesModel CompressionObject Localizationreinforcement-learning+3C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors. Many WSOD approaches adopt multipl…
Multiple Instance LearningObjectobject-detectionObject Detection+3Embodied Visual Recognition
Passive visual systems typically fail to recognize objects in the amodal setting where they are heavily occluded. In contrast, humans and other embodied agents have the ability to move in the environment, and actively co…
ObjectObject LocalizationSemantic SegmentationLearning to Augment Synthetic Images for Sim2Real Policy Transfer
Vision and learning have made significant progress that could improve robotics policies for complex tasks and environments. Learning deep neural networks for image understanding, however, requires large amounts of domain…
Object LocalizationUnsupervised Traffic Accident Detection in First-Person Videos
Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems. However, most work on video anomaly de…
Anomaly DetectionAutonomous DrivingObject LocalizationOne-Class Classification+3Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks
Lesions characterized by computed tomography (CT) scans, are arguably often elliptical objects. However, current lesion detection systems are predominantly adopted from the popular Region Proposal Networks (RPNs) that on…
Computed Tomography (CT)Lesion DetectionObject LocalizationRegion ProposalMin-Entropy Latent Model for Weakly Supervised Object Detection
Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors. The inconsistency between the weak su…
image-classificationImage ClassificationObjectobject-detection+4Photorealistic Image Synthesis for Object Instance Detection
We present an approach to synthesize highly photorealistic images of 3D object models, which we use to train a convolutional neural network for detecting the objects in real images. The proposed approach has three key in…
6D Pose Estimation6D Pose Estimation using RGBImage GenerationObject+1A Smoother Way to Train Structured Prediction Models
We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon. Smoothing overcomes the non-smoothness inherent to the maximum margin structured prediction…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Object Localization+2SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images
In this paper, we present a large-scale dataset and establish a baseline for prohibited item discovery in Security Inspection X-ray images. Our dataset, named SIXray, consists of 1,059,231 X-ray images, in which 6 classe…
Object LocalizationLearning from Web Data: the Benefit of Unsupervised Object Localization
Annotating a large number of training images is very time-consuming. In this background, this paper focuses on learning from easy-to-acquire web data and utilizes the learned model for fine-grained image classification i…
Fine-Grained Image ClassificationGeneral Classificationimage-classificationImage Classification+3Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn. Coarse-to-fine (C2F) learning is a promising direction, but …
image-classificationImage ClassificationObject LocalizationSemantic SegmentationGrid R-CNN
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid …
2D Object DetectionNovel Object DetectionObjectobject-detection+2Calibrating Uncertainties in Object Localization Task
In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, su…
Autonomous DrivingDecision MakingObjectobject-detection+3MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization
Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a single RGB image due to the geometric information loss during imagery pro…
2D Object Detection3D Object DetectionDepth EstimationMonocular 3D Object Detection+5