Papers Weakly-Supervised Object Localization
“Weakly-Supervised Object Localization” 태그가 달린 논문 140편 · 필터 해제
Pro2SAM: Mask Prompt to SAM with Grid Points for Weakly Supervised Object Localization
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. Current studies focus o…
Object LocalizationWeakly-Supervised Object LocalizationZero-shot GeneralizationPixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization
Weakly supervised object localization (WSOL) methods allow training models to classify images and localize ROIs. WSOL only requires low-cost image-class annotations yet provides a visually interpretable classifier, which…
image-classificationImage ClassificationObject LocalizationWeakly-Supervised Object LocalizationTeD-Loc: Text Distillation for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) using classification models trained with only image-class labels remains an important challenge in computer vision. Given their reliance on classification objectives, traditio…
ClassificationDenoisingMultiple Instance LearningObject Localization+2Categorical Knowledge Fused Recognition: Fusing Hierarchical Knowledge with Image Classification through Aligning and Deep Metric Learning
Image classification is a fundamental computer vision task and an important baseline for deep metric learning. In decades efforts have been made on enhancing image classification accuracy by using deep learning models wh…
Classificationimage-classificationImage ClassificationMetric Learning+3Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos
Weakly-Supervised Video Object Localization (WSVOL) involves localizing an object in videos using only video-level labels, also referred to as tags. State-of-the-art WSVOL methods like Temporal CAM (TCAM) rely on class a…
Object LocalizationWeakly-Supervised Object LocalizationLeveraging Activations for Superpixel Explanations
Saliency methods have become standard in the explanation toolkit of deep neural networks. Recent developments specific to image classifiers have investigated region-based explanations with either new methods or by adapti…
Object LocalizationSuperpixelsWeakly-Supervised Object LocalizationSE3D: A Framework For Saliency Method Evaluation In 3D Imaging
For more than a decade, deep learning models have been dominating in various 2D imaging tasks. Their application is now extending to 3D imaging, with 3D Convolutional Neural Networks (3D CNNs) being able to process LIDAR…
3D Classification3D Object Detection3D Semantic SegmentationAutonomous Driving+3Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology
Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology …
Contrastive LearningDomain AdaptationObject LocalizationSource-Free Domain Adaptation+2Improving Weakly-Supervised Object Localization Using Adversarial Erasing and Pseudo Label
Weakly-supervised learning approaches have gained significant attention due to their ability to reduce the effort required for human annotations in training neural networks. This paper investigates a framework for weakly…
ObjectObject LocalizationPseudo LabelWeakly-supervised Learning+1A Realistic Protocol for Evaluation of Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) allows training deep learning models for classification and localization (LOC) using only global class-level labels. The absence of bounding box (bbox) supervision during trai…
Model SelectionObjectObject LocalizationRegion Proposal+1Towards Two-Stream Foveation-based Active Vision Learning
Deep neural network (DNN) based machine perception frameworks process the entire input in a one-shot manner to provide answers to both "what object is being observed" and "where it is located". In contrast, the "two-stre…
FoveationObjectObject LocalizationWeakly-Supervised Object LocalizationCAM Back Again: Large Kernel CNNs from a Weakly Supervised Object Localization Perspective
Recently, convolutional neural networks (CNNs) with large size kernels have attracted much attention in the computer vision field, following the success of the Vision Transformers. Large kernel CNNs have been reported to…
Data AugmentationObject LocalizationWeakly-Supervised Object LocalizationExploring Foveation and Saccade for Improved Weakly-Supervised Localization
Deep neural networks have become the de facto choice as feature extraction engines, ubiquitously used for computer vision tasks. The current approach is to process every input with uniform resolution in a one-shot manner…
Active Object LocalizationFoveationObject LocalizationWeakly-Supervised Object LocalizationMultiscale Vision Transformer With Deep Clustering-Guided Refinement for Weakly Supervised Object Localization
This work addresses the task of weakly-supervised object localization. The goal is to learn object localization using only image-level class labels, which are much easier to obtain compared to bounding box annotations. T…
ClusteringDeep ClusteringObjectObject Localization+1DiPS: Discriminative Pseudo-Label Sampling with Self-Supervised Transformers for Weakly Supervised Object Localization
Self-supervised vision transformers (SSTs) have shown great potential to yield rich localization maps that highlight different objects in an image. However, these maps remain class-agnostic since the model is unsupervise…
ObjectObject LocalizationPseudo LabelWeakly-Supervised Object LocalizationBackground Activation Suppression for Weakly Supervised Object Localization and Semantic Segmentation
Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. Recently, a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve pixel…
ObjectObject LocalizationSemantic SegmentationWeakly-Supervised Object Localization+2FDCNet: Feature Drift Compensation Network for Class-Incremental Weakly Supervised Object Localization
This work addresses the task of class-incremental weakly supervised object localization (CI-WSOL). The goal is to incrementally learn object localization for novel classes using only image-level annotations while retaini…
class-incremental learningIncremental LearningKnowledge DistillationObject+2Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision. Due to the local receptive fields generated by convolution operations, previous CNN-based methods suffe…
ObjectObject LocalizationWeakly-Supervised Object LocalizationRethinking the Localization in Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision. This task is to localize the objects in the images given only the image-level supervision. Recently, divid…
ObjectObject LocalizationWeakly-Supervised Object LocalizationGenerative Prompt Model for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) remains challenging when learning object localization models from image category labels. Conventional methods that discriminatively train activation models ignore representati…
DenoisingImage DenoisingLanguage ModelingLanguage Modelling+4