Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Learning Consistency from High-quality Pseudo-labels for Weakly Supervised Object Localization
Pseudo-supervised learning methods have been shown to be effective for weakly supervised object localization tasks. However, the effectiveness depends on the powerful regularization ability of deep neural networks. Based…
Object LocalizationPseudo LabelWeakly-Supervised Object LocalizationObject Localization under Single Coarse Point Supervision
Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotation mode inevitably introduces semantic va…
Multiple Instance LearningObjectObject LocalizationMulti-class Token Transformer for Weakly Supervised Semantic Segmentation
This paper proposes a new transformer-based framework to learn class-specific object localization maps as pseudo labels for weakly supervised semantic segmentation (WSSS). Inspired by the fact that the attended regions o…
ObjectObject LocalizationSemantic SegmentationWeakly supervised Semantic Segmentation+1Weakly Supervised Object Localization as Domain Adaption
Weakly supervised object localization (WSOL) focuses on localizing objects only with the supervision of image-level classification masks. Most previous WSOL methods follow the classification activation map (CAM) that loc…
ClassificationDomain AdaptationObjectObject Localization+1Learning Transferable Reward for Query Object Localization with Policy Adaptation
We propose a reinforcement learning based approach to query object localization, for which an agent is trained to localize objects of interest specified by a small exemplary set. We learn a transferable reward signal for…
Metric LearningObject Localizationreinforcement-learningReinforcement Learning (RL)Crafting Better Contrastive Views for Siamese Representation Learning
Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims at minimizing distances between positive pairs. For high performance Siamese representation learning, one of the ke…
Contrastive LearningObject LocalizationRepresentation LearningWebly Supervised Concept Expansion for General Purpose Vision Models
General Purpose Vision (GPV) systems are models that are designed to solve a wide array of visual tasks without requiring architectural changes. Today, GPVs primarily learn both skills and concepts from large fully super…
Human-Object Interaction DetectionImage RetrievalObject CategorizationObject Localization+2Probing the Role of Positional Information in Vision-Language Models
In most Vision-Language models (VL) the understanding of the image structure is enabled by injecting the position information (PI) about objects in the image. In our case study of LXMERT, a state-of-the-art VL model, we …
Contrastive LearningImage-text matchingObject LocalizationPosition+4CaFT: Clustering and Filter on Tokens of Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) is a challenging task to localize the object by only category labels. However, there is contradiction between classification and localization because accurate classification n…
ClusteringObjectObject LocalizationWeakly-Supervised Object LocalizationC2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on dis…
Contrastive Learningimage-classificationImage ClassificationObject+3P2P-Loc: Point to Point Tiny Person Localization
Bounding-box annotation form has been the most frequently used method for visual object localization tasks. However, bounding-box annotation relies on a large amount of precisely annotating bounding boxes, and it is expe…
ObjectObject LocalizationBackground-aware Classification Activation Map for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) relaxes the requirement of dense annotations for object localization by using image-level classification masks to supervise its learning process. However, current WSOL methods…
ClassificationObjectObject LocalizationWeakly-Supervised Object LocalizationThe Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object detection task and reformulate it as th…
3D Object DetectionAutonomous DrivingMonocular 3D Object DetectionObject+4Class-aware Sounding Objects Localization via Audiovisual Correspondence
Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achieve class-aware sounding objects localiza…
Objectobject-detectionObject DetectionObject Localization+1ScanQA: 3D Question Answering for Spatial Scene Understanding
We propose a new 3D spatial understanding task of 3D Question Answering (3D-QA). In the 3D-QA task, models receive visual information from the entire 3D scene of the rich RGB-D indoor scan and answer the given textual qu…
3D Question Answering (3D-QA)ObjectObject LocalizationQuestion Answering+4A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
This work presents a simple vision transformer design as a strong baseline for object localization and instance segmentation tasks. Transformers recently demonstrate competitive performance in image classification tasks.…
image-classificationImage ClassificationInstance SegmentationObject+5LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative p…
Inductive BiasObjectObject LocalizationWeakly-Supervised Object LocalizationModelling Lips-State Detection Using CNN for Non-Verbal Communications
Vision-based deep learning models can be promising for speech-and-hearing-impaired and secret communications. While such non-verbal communications are primarily investigated with hand-gestures and facial expressions, no …
License Plate DetectionLicense Plate RecognitionObject LocalizationOptical Character Recognition (OCR)Background Activation Suppression for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects using only image-level labels. Recently a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve localization task. Exist…
ObjectObject LocalizationWeakly-Supervised Object LocalizationGroup-Wise Learning for Weakly Supervised Semantic Segmentation
Acquiring sufficient ground-truth supervision to train deep visual models has been a bottleneck over the years due to the data-hungry nature of deep learning. This is exacerbated in some structured prediction tasks, such…
Graph Neural NetworkObject LocalizationSegmentationSemantic Segmentation+5