Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
I see what you hear: a vision-inspired method to localize words
This paper explores the possibility of using visual object detection techniques for word localization in speech data. Object detection has been thoroughly studied in the contemporary literature for visual data. Noting th…
Objectobject-detectionObject DetectionObject LocalizationPerceptual Grouping in Contrastive Vision-Language Models
Recent advances in zero-shot image recognition suggest that vision-language models learn generic visual representations with a high degree of semantic information that may be arbitrarily probed with natural language phra…
Object LocalizationRepresentation LearningUnsupervised Semantic SegmentationUnsupervised Semantic Segmentation with Language-image Pre-trainingPrototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
Most existing 3D point cloud object detection approaches heavily rely on large amounts of labeled training data. However, the labeling process is costly and time-consuming. This paper considers few-shot 3D point cloud ob…
Objectobject-detectionObject DetectionObject LocalizationDoes Thermal Really Always Matter for RGB-T Salient Object Detection?
In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most …
object-detectionObject DetectionObject LocalizationRGB-T Salient Object Detection+1Dual Progressive Transformations for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current state-of-the-art CNN-based methods usually a…
Inductive BiasObjectObject LocalizationSemantic Segmentation+3Contrastive Corpus Attribution for Explaining Representations
Despite the widespread use of unsupervised models, very few methods are designed to explain them. Most explanation methods explain a scalar model output. However, unsupervised models output representation vectors, the el…
Contrastive LearningObject LocalizationScale-Invariant Fast Functional Registration
Functional registration algorithms represent point clouds as functions (e.g. spacial occupancy field) avoiding unreliable correspondence estimation in conventional least-squares registration algorithms. However, existing…
Object LocalizationNeRF-Loc: Transformer-Based Object Localization Within Neural Radiance Fields
Neural Radiance Fields (NeRFs) have become a widely-applied scene representation technique in recent years, showing advantages for robot navigation and manipulation tasks. To further advance the utility of NeRFs for robo…
NeRFObject LocalizationRobot NavigationAn Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving Videos
Detecting dangerous traffic agents in videos captured by vehicle-mounted dashboard cameras (dashcams) is essential to facilitate safe navigation in a complex environment. Accident-related videos are just a minor portion …
Anomaly DetectionObjectObject LocalizationOptical Flow Estimation+1Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking
Learning robust feature matching between the template and search area is crucial for 3D Siamese tracking. The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between …
Object LocalizationObject TrackingPoint Cloud RegistrationConstrained Sampling for Class-Agnostic Weakly Supervised Object Localization
Self-supervised vision transformers can generate accurate localization maps of the objects in an image. However, since they decompose the scene into multiple maps containing various objects, and they do not rely on any e…
ObjectObject LocalizationWeakly-Supervised Object LocalizationDiscriminative Sampling of Proposals in Self-Supervised Transformers for Weakly Supervised Object Localization
Drones are employed in a growing number of visual recognition applications. A recent development in cell tower inspection is drone-based asset surveillance, where the autonomous flight of a drone is guided by localizing …
ObjectObject LocalizationWeakly-Supervised Object LocalizationProgressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery
State-of-the-art object detection methods applied to satellite and drone imagery largely fail to identify small and dense objects. One reason is the high variability of content in the overhead imagery due to the terrestr…
Contrastive LearningDomain AdaptationObjectobject-detection+4Geometry Aligned Variational Transformer for Image-conditioned Layout Generation
Layout generation is a novel task in computer vision, which combines the challenges in both object localization and aesthetic appraisal, widely used in advertisements, posters, and slides design. An accurate and pleasant…
Layout DesignLayout GenerationObject LocalizationTCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos
Weakly supervised video object localization (WSVOL) allows locating object in videos using only global video tags such as object class. State-of-art methods rely on multiple independent stages, where initial spatio-tempo…
ObjectObject LocalizationObject TrackingOptical Flow Estimation+1Detect and Approach: Close-Range Navigation Support for People with Blindness and Low Vision
People with blindness and low vision (pBLV) experience significant challenges when locating final destinations or targeting specific objects in unfamiliar environments. Furthermore, besides initially locating and orienti…
ObjectObject LocalizationObject Detection with Deep Reinforcement Learning
Object localization has been a crucial task in computer vision field. Methods of localizing objects in an image have been proposed based on the features of the attended pixels. Recently researchers have proposed methods …
Active Object LocalizationDeep Reinforcement LearningObjectobject-detection+5TransPillars: Coarse-to-Fine Aggregation for Multi-Frame 3D Object Detection
3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics. However, most existing studies focus on single point cloud frames without harness…
3D Object DetectionAutonomous DrivingObjectobject-detection+2Re-Attention Transformer for Weakly Supervised Object Localization
Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories. Existing deep network approaches are mainly based on class activation map, whic…
ObjectObject LocalizationWeakly-Supervised Object LocalizationWeakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
Weakly Supervised Object Localization (WSOL), which aims to localize objects by only using image-level labels, has attracted much attention because of its low annotation cost in real applications. Recent studies leverage…
Long-range modelingObjectObject LocalizationWeakly-Supervised Object Localization