Papers Scene Labeling
“Scene Labeling” 태그가 달린 논문 45편 · 필터 해제
Towards Panoptic 3D Parsing for Single Image in the Wild
Performing single image holistic understanding and 3D reconstruction is a central task in computer vision. This paper presents an integrated system that performs dense scene labeling, object detection, instance segmentat…
3D Reconstruction3D Shape ReconstructionAutonomous DrivingDepth Estimation+7Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling
Existing unsupervised person re-identification methods only rely on visual clues to match pedestrians under different cameras. Since visual data is essentially susceptible to occlusion, blur, clothing changes, etc., a pr…
Graph Neural NetworkPerson Re-IdentificationScene LabelingUnsupervised Person Re-IdentificationMinimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration
This paper proposes minimal solvers that use combinations of imaged translational symmetries and parallel scene lines to jointly estimate lens undistortion with either affine rectification or focal length and absolute or…
Camera Auto-CalibrationScene LabelingScene ParsingA Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks
An interesting development in automatic visual recognition has been the emergence of tasks where it is not possible to assign objective labels to images, yet still feasible to collect annotations that reflect human judge…
Age EstimationAttributeimage-classificationImage Classification+2Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation
We achieve 3D semantic scene labeling by exploring semantic relation between each point and its contextual neighbors through edges. Besides an encoder-decoder branch for predicting point labels, we construct an edge bran…
DecoderScene LabelingSemantic SegmentationEnd-to-End 3D-PointCloud Semantic Segmentation for Autonomous Driving
3D semantic scene labeling is a fundamental task for Autonomous Driving. Recent work shows the capability of Deep Neural Networks in labeling 3D point sets provided by sensors like LiDAR, and Radar. Imbalanced distributi…
Autonomous DrivingScene LabelingSemantic SegmentationTransfer LearningA Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling
Street scene understanding is an essential task for autonomous driving. One important step towards this direction is scene labeling, which annotates each pixel in the images with a correct class label. Although many appr…
Autonomous DrivingData AugmentationScene LabelingScene UnderstandingRGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion
RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting the performance of 3D semantic scene com…
3D Semantic Scene CompletionScene LabelingScene Parsing via Dense Recurrent Neural Networks with Attentional Selection
Recurrent neural networks (RNNs) have shown the ability to improve scene parsing through capturing long-range dependencies among image units. In this paper, we propose dense RNNs for scene labeling by exploring various l…
Scene LabelingScene ParsingCombining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling
Modern deep learning algorithms have triggered various image segmentation approaches. However most of them deal with pixel based segmentation. However, superpixels provide a certain degree of contextual information while…
Image SegmentationScene LabelingSegmentationSemantic Segmentation+1Multimodal Recurrent Neural Networks with Information Transfer Layers for Indoor Scene Labeling
This paper proposes a new method called Multimodal RNNs for RGB-D scene semantic segmentation. It is optimized to classify image pixels given two input sources: RGB color channels and Depth maps. It simultaneously perfor…
Scene LabelingSemantic SegmentationDense Recurrent Neural Networks for Scene Labeling
Recently recurrent neural networks (RNNs) have demonstrated the ability to improve scene labeling through capturing long-range dependencies among image units. In this paper, we propose dense RNNs for scene labeling by ex…
Scene LabelingGuided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. Ho…
Scene LabelingSemantic SegmentationHierarchical Scene Parsing by Weakly Supervised Learning with Image Descriptions
This paper investigates a fundamental problem of scene understanding: how to parse a scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations). We propose a deep ar…
DescriptiveObjectScene LabelingScene Parsing+3Exploring Directional Path-Consistency for Solving Constraint Networks
Among the local consistency techniques used for solving constraint networks, path-consistency (PC) has received a great deal of attention. However, enforcing PC is computationally expensive and sometimes even unnecessary…
Scene LabelingUnsupervised Semantic Scene Labeling for Streaming Data
We introduce an unsupervised semantic scene labeling approach that continuously learns and adapts semantic models discovered within a data stream. While closely related to unsupervised video segmentation, our algorithm i…
Scene LabelingSegmentationVideo SegmentationVideo Semantic SegmentationEpisodic CAMN: Contextual Attention-Based Memory Networks With Iterative Feedback for Scene Labeling
Scene labeling can be seen as a sequence-sequence prediction task (pixels-labels), and it is quite important to leverage relevant context to enhance the performance of pixel classification. In this paper, we introduce an…
General ClassificationScene LabelingLearning Deep Representations for Scene Labeling with Semantic Context Guided Supervision
Scene labeling is a challenging classification problem where each input image requires a pixel-level prediction map. Recently, deep-learning-based methods have shown their effectiveness on solving this problem. However, …
Scene LabelingDeep Contextual Recurrent Residual Networks for Scene Labeling
Designed as extremely deep architectures, deep residual networks which provide a rich visual representation and offer robust convergence behaviors have recently achieved exceptional performance in numerous computer visio…
Representation LearningScene LabelingSelf corrective Perturbations for Semantic Segmentation and Classification
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. Ho…
ClassificationGeneral ClassificationScene LabelingSemantic Segmentation