Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Category-aware Allocation Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects based on only image-level labels as supervision. Recently, transformers have been introduced into WSOL, yielding impressive results. The self-atte…
ObjectObject LocalizationWeakly-Supervised Object LocalizationUnsupervised Object Localization: Observing the Background to Discover Objects
Recent advances in self-supervised visual representation learning have paved the way for unsupervised methods tackling tasks such as object discovery and instance segmentation. However, discovering objects in an image wi…
Instance SegmentationObjectObject DiscoveryObject Localization+7EgoLoc: Revisiting 3D Object Localization from Egocentric Videos with Visual Queries
With the recent advances in video and 3D understanding, novel 4D spatio-temporal methods fusing both concepts have emerged. Towards this direction, the Ego4D Episodic Memory Benchmark proposed a task for Visual Queries w…
3D ReconstructionObjectObject LocalizationRetrievalDeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering
Image segmentation is a fundamental task in computer vision. Data annotation for training supervised methods can be labor-intensive, motivating unsupervised methods. Current approaches often rely on extracting deep featu…
ClusteringGraph Neural NetworkImage SegmentationObject Localization+5Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks. Over the years, the research community expands mix-up methods into two directions, with extensive ef…
Classifier calibrationImage ClassificationObject LocalizationWeakly-Supervised Object LocalizationSemi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty
Semi-supervised object detection (SSOD) aims to boost detection performance by leveraging extra unlabeled data. The teacher-student framework has been shown to be promising for SSOD, in which a teacher network generates …
ClassificationContrastive LearningObjectobject-detection+7D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation
Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes leads to substandard object detection and …
Domain AdaptationObjectobject-detectionObject Detection+2Multimodal Query-guided Object Localization
Consider a scenario in one-shot query-guided object localization where neither an image of the object nor the object category name is available as a query. In such a scenario, a hand-drawn sketch of the object could be a…
ObjectObject LocalizationRegion ProposalLocPoseNet: Robust Location Prior for Unseen Object Pose Estimation
Object location prior is critical for the standard 6D object pose estimation setting. The prior can be used to initialize the 3D object translation and facilitate 3D object rotation estimation. Unfortunately, the object …
6D Pose Estimation6D Pose Estimation using RGBObjectObject Localization+3MUSTER: A Multi-scale Transformer-based Decoder for Semantic Segmentation
In recent works on semantic segmentation, there has been a significant focus on designing and integrating transformer-based encoders. However, less attention has been given to transformer-based decoders. We emphasize tha…
DecoderObject LocalizationSegmentationSemantic SegmentationRoboflow 100: A Rich, Multi-Domain Object Detection Benchmark
The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these d…
2D Object DetectionImage RetrievalMedical Object DetectionMulti-Object Tracking+14One-Shot General Object Localization
This paper presents a general one-shot object localization algorithm called OneLoc. Current one-shot object localization or detection methods either rely on a slow exhaustive feature matching process or lack the ability …
ObjectObject LocalizationAutonomous Marker-less Rapid Aerial Grasping
In a future with autonomous robots, visual and spatial perception is of utmost importance for robotic systems. Particularly for aerial robotics, there are many applications where utilizing visual perception is necessary …
ObjectObject LocalizationScene SegmentationBoundary-aware Camouflaged Object Detection via Deformable Point Sampling
The camouflaged object detection (COD) task aims to identify and segment objects that blend into the background due to their similar color or texture. Despite the inherent difficulties of the task, COD has gained conside…
Objectobject-detectionObject DetectionObject LocalizationUnifying Vision-Language Representation Space with Single-tower Transformer
Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this paper, we explore the bold hypothesis that an image and its caption can be simply regard…
Contrastive LearningObject LocalizationRepresentation LearningRetrieval+1Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric
Combining the Color and Event cameras (also called Dynamic Vision Sensors, DVS) for robust object tracking is a newly emerging research topic in recent years. Existing color-event tracking framework usually contains mult…
Object LocalizationObject TrackingA Low-Shot Object Counting Network With Iterative Prototype Adaptation
We consider low-shot counting of arbitrary semantic categories in the image using only few annotated exemplars (few-shot) or no exemplars (no-shot). The standard few-shot pipeline follows extraction of appearance queries…
Exemplar-Free CountingObjectObject CountingObject LocalizationScene-Text Oriented Reffering Expression Comprehension
Abstract—Referring expression comprehension (REC) aims to identify and locate a specific object in visual scenes referred to by a natural language expression. Existing studies of REC only focus on basic visual attribu…
Object LocalizationReferring ExpressionReferring Expression ComprehensionGrounding Scene Graphs on Natural Images via Visio-Lingual Message Passing
This paper presents a framework for jointly grounding objects that follow certain semantic relationship constraints given in a scene graph. A typical natural scene contains several objects, often exhibiting visual relati…
Graph Neural NetworkObjectObject LocalizationRobust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)
Recently, the availability of remote sensing imagery from aerial vehicles and satellites constantly improved. For an automated interpretation of such data, deep-learning-based object detectors achieve state-of-the-art pe…
Objectobject-detectionObject DetectionObject Localization+1