Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
Despite the remarkable progress, weakly supervised segmentation approaches are still inferior to their fully supervised counterparts. We obverse the performance gap mainly comes from their limitation on learning to produ…
ObjectObject LocalizationSegmentationSemantic Segmentation+2Learning to Grasp Without Seeing
Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physica…
Object LocalizationAdapting Mask-RCNN for Automatic Nucleus Segmentation
Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask-RCNN is a recently proposed state-of-the-art algorit…
Instance SegmentationObjectobject-detectionObject Detection+3Localization: A Missing Link in the Pipeline of Object Matching and Registration
Image registration is a process of aligning two or more images of same objects using geometric transformation. Most of the existing approaches work on the assumption of location invariance. These approaches require objec…
Image RegistrationObjectObject LocalizationAdversarial Complementary Learning for Weakly Supervised Object Localization
In this work, we propose Adversarial Complementary Learning (ACoL) to automatically localize integral objects of semantic interest with weak supervision. We first mathematically prove that class localization maps can be …
General ClassificationObjectObject LocalizationWeakly-Supervised Object LocalizationFusing Saliency Maps with Region Proposals for Unsupervised Object Localization
In this paper we address the problem of unsupervised localization of objects in single images. Compared to previous state-of-the-art method our method is fully unsupervised in the sense that there is no prior instance le…
DecoderObject LocalizationRegion ProposalUnsupervised Object LocalizationFPAN: Fine-grained and Progressive Attention Localization Network for Data Retrieval
The Localization of the target object for data retrieval is a key issue in the Intelligent and Connected Transportation Systems (ICTS). However, due to lack of intelligence in the traditional transportation system, it ca…
Multi-Task LearningObjectObject LocalizationObject Tracking+1Exploring to learn visual saliency: The RL-IAC approach
The problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual saliency directly on a robot. This model, lear…
Object LocalizationReinforcement LearningWeakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset
We propose a weakly supervised method using two algorithms to predict object bounding boxes given only an image classification dataset. First algorithm is a simple Fully Convolutional Network (FCN) trained to classify ob…
Decoderimage-classificationImage ClassificationObject+5A Non-Technical Survey on Deep Convolutional Neural Network Architectures
Artificial neural networks have recently shown great results in many disciplines and a variety of applications, including natural language understanding, speech processing, games and image data generation. One particular…
image-classificationImage ClassificationNatural Language UnderstandingObject+5The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera
Accurate localization of other traffic participants is a vital task in autonomous driving systems. State-of-the-art systems employ a combination of sensing modalities such as RGB cameras and LiDARs for localizing traffic…
Autonomous DrivingMonocular ReconstructionObject LocalizationTell Me Where to Look: Guided Attention Inference Network
Weakly supervised learning with only coarse labels can obtain visual explanations of deep neural network such as attention maps by back-propagating gradients. These attention maps are then available as priors for tasks s…
Object LocalizationSemantic SegmentationWeakly-supervised LearningMulti-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning
Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predi…
ClusteringGeneral Classificationimage-classificationImage Classification+15Attention-Aware Generative Adversarial Networks (ATA-GANs)
In this work, we present a novel approach for training Generative Adversarial Networks (GANs). Using the attention maps produced by a Teacher- Network we are able to improve the quality of the generated images as well as…
object-detectionObject DetectionObject LocalizationTransfer LearningImproved Techniques For Weakly-Supervised Object Localization
We propose an improved technique for weakly-supervised object localization. Conventional methods have a limitation that they focus only on most discriminative parts of the target objects. The recent study addressed this …
Data AugmentationObjectObject LocalizationWeakly-Supervised Object LocalizationTiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
Object detection is a major challenge in computer vision, involving both object classification and object localization within a scene. While deep neural networks have been shown in recent years to yield very powerful tec…
Objectobject-detectionObject DetectionObject Localization+1End-to-end detection-segmentation network with ROI convolution
We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as…
Object LocalizationSegmentationParticle filter re-detection for visual tracking via correlation filters
Most of the correlation filter based tracking algorithms can achieve good performance and maintain fast computational speed. However, in some complicated tracking scenes, there is a fatal defect that causes the object to…
ObjectObject LocalizationVisual TrackingDistance to Center of Mass Encoding for Instance Segmentation
The instance segmentation can be considered an extension of the object detection problem where bounding boxes are replaced by object contours. Strictly speaking the problem requires to identify each pixel instance and cl…
Instance SegmentationObjectobject-detectionObject Detection+3Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. While previous methods focus on images or 3D voxels, often obscuring natural 3D patterns and invariances of 3D data, we direct…
3D Object DetectionObjectObject DetectionObject Detection In Indoor Scenes+2