Papers Weakly-Supervised Object Localization
“Weakly-Supervised Object Localization” 태그가 달린 논문 140편 · 필터 해제
Total Variation Optimization Layers for Computer Vision
Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization probl…
DenoisingEdge DetectionGPUimage-classification+5Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization
Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels. Most existing methods use a class activation map (CAM) to generate a loca…
ObjectObject LocalizationWeakly-Supervised Object LocalizationHINT: Hierarchical Neuron Concept Explainer
To interpret deep networks, one main approach is to associate neurons with human-understandable concepts. However, existing methods often ignore the inherent relationships of different concepts (e.g., dog and cat both be…
Object LocalizationWeakly-Supervised Object LocalizationContrastive 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 discrim…
Contrastive Learningimage-classificationImage ClassificationObject+3Learning 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 LocalizationWeakly 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+1Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this paper, we demonstrate a graph-based appr…
Objectobject-detectionObject DetectionObject Discovery+5CaFT: 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+3Background-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 LocalizationLCTR: 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 LocalizationBackground 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+5TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs
Attention modules for Convolutional Neural Networks (CNNs) are an effective method to enhance performance on multiple computer-vision tasks. While existing methods appropriately model channel-, spatial- and self-attentio…
feature selectionImage ClassificationMulti-Label ClassificationObject Localization+2SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost
The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps (CAM) to generate pseudo masks as ground-truth. However, the existing methods typically depend on the painstaking trai…
Object LocalizationSemantic SegmentationWeakly-Supervised Object LocalizationWeakly supervised Semantic Segmentation+1Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization
We present a two-stage learning framework for weakly supervised object localization (WSOL). While most previous efforts rely on high-level feature based CAMs (Class Activation Maps), this paper proposes to localize objec…
ObjectObject LocalizationWeakly-Supervised Object LocalizationLocalizing Objects with Self-Supervised Transformers and no Labels
Localizing objects in image collections without supervision can help to avoid expensive annotation campaigns. We propose a simple approach to this problem, that leverages the activation features of a vision transformer p…
ObjectObject DiscoverySingle-object discoveryWeakly-Supervised Object LocalizationF-CAM: Full Resolution Class Activation Maps via Guided Parametric Upscaling
Class Activation Mapping (CAM) methods have recently gained much attention for weakly-supervised object localization (WSOL) tasks. They allow for CNN visualization and interpretation without training on fully annotated i…
Object LocalizationWeakly-Supervised Object LocalizationCausal Explanation of Convolutional Neural Networks
In this paper we introduce an explanation technique for Convolutional Neural Networks (CNNs) based on the theory of causality by Halpern and Pearl [12]. The causal explanation technique (CexCNN) is based on measuring the…
counterfactualCounterfactual ExplanationCounterfactual ReasoningModel Compression+2Self-Taught Cross-Domain Few-Shot Learning with Weakly Supervised Object Localization and Task-Decomposition
The domain shift between the source and target domain is the main challenge in Cross-Domain Few-Shot Learning (CD-FSL). However, the target domain is absolutely unknown during the training on the source domain, which res…
Cross-Domain Few-Shotcross-domain few-shot learningFew-Shot LearningObject Localization+1