Papers Weakly-Supervised Object Localization
“Weakly-Supervised Object Localization” 태그가 달린 논문 140편 · 필터 해제
In-sample Contrastive Learning and Consistent Attention for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize the target object using only the image-level supervision. Recent methods encourage the model to activate feature maps over the entire object by dropping the m…
Contrastive LearningObjectObject LocalizationWeakly-Supervised Object LocalizationEntropy Guided Adversarial Model for Weakly Supervised Object Localization
Weakly Supervised Object Localization is challenging because of the lack of bounding box annotations. Previous works tend to generate a class activation map i.e CAM to localize the object. Unfortunately, the network acti…
ObjectObject LocalizationWeakly-Supervised Object LocalizationEigen-CAM: Class Activation Map using Principal Components
Deep neural networks are ubiquitous due to the ease of developing models and their influence on other domains. At the heart of this progress is convolutional neural networks (CNNs) that are capable of learning representa…
Object LocalizationWeakly-Supervised Object LocalizationRethinking Class Activation Mapping for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) is a task of localizing an object in an image only using image-level labels. To tackle the WSOL problem, most previous studies have followed the conventional class activation …
ObjectObject LocalizationWeakly-Supervised Object LocalizationA Generic Visualization Approach for Convolutional Neural Networks
Retrieval networks are essential for searching and indexing. Compared to classification networks, attention visualization for retrieval networks is hardly studied. We formulate attention visualization as a constrained op…
ClassificationGeneral ClassificationObject LocalizationRetrieval+1Geometry Constrained Weakly Supervised Object Localization
We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization (WSOL). GC-Net consists of three modules: a detector, a generator and a classifier. The detector predicts the object loc…
ObjectObject LocalizationWeakly-Supervised Object LocalizationEvaluation for Weakly Supervised Object Localization: Protocol, Metrics, and Datasets
Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal WSOL work of class activation mapping (C…
Few-Shot LearningModel SelectionObject LocalizationWeakly-Supervised Object LocalizationRethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps
Recently, remarkable progress has been made in weakly supervised object localization (WSOL) to promote object localization maps. The common practice of evaluating these maps applies an indirect and coarse way, i.e., obta…
ObjectObject LocalizationWeakly-Supervised Object LocalizationErasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize object with only weak supervision like image-level labels. However, a long-standing problem for available techniques based on the classification network is th…
ClassificationGeneral Classificationimage-classificationImage Classification+3Distilling Knowledge from Refinement in Multiple Instance Detection Networks
Weakly supervised object detection (WSOD) aims to tackle the object detection problem using only labeled image categories as supervision. A common approach used in WSOD to deal with the lack of localization information i…
Knowledge DistillationMultiple Instance LearningObjectobject-detection+4Pairwise Similarity Knowledge Transfer for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) methods only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms. We study the problem of learning localization…
Object LocalizationTransfer LearningWeakly-Supervised Object LocalizationDual-attention Guided Dropblock Module for Weakly Supervised Object Localization
Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL…
Object LocalizationWeakly-Supervised Object LocalizationWeakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning
Few-shot learning (FSL) aims to learn novel visual categories from very few samples, which is a challenging problem in real-world applications. Many methods of few-shot classification work well on general images to learn…
ClassificationFew-Shot LearningGeneral ClassificationObject Localization+1Rethinking the Route Towards Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels. Previous methods often try to utilize feature maps and classification weights to localize objects using image level anno…
General ClassificationObjectObject LocalizationWeakly-Supervised Object LocalizationAn interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be app…
Breast Cancer DetectionGPULesion SegmentationMedical Diagnosis+2Evaluating Weakly Supervised Object Localization Methods Right
Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal WSOL work of class activation mapping (C…
Few-Shot LearningModel SelectionObjectObject Localization+1Convolutional STN for Weakly Supervised Object Localization
Weakly supervised object localization is a challenging task in which the object of interest should be localized while learning its appearance. State-of-the-art methods recycle the architecture of a standard CNN by using …
ObjectObject LocalizationWeakly-Supervised Object LocalizationAttributional Robustness Training using Input-Gradient Spatial Alignment
Interpretability is an emerging area of research in trustworthy machine learning. Safe deployment of machine learning system mandates that the prediction and its explanation be reliable and robust. Recently, it has been …
BIG-bench Machine LearningObject LocalizationTripletWeakly-Supervised Object LocalizationRethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator
Mutual information is widely applied to learn latent representations of observations, whilst its implication in classification neural networks remain to be better explained. We show that optimising the parameters of clas…
ClassificationFine-Grained Image ClassificationGeneral Classificationimage-classification+2Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation
Weakly Supervised Object Localization (WSOL) methods generate both classification and localization results by learning from only image category labels. Previous methods usually utilize class activation map (CAM) to obtai…
ObjectObject LocalizationWeakly-Supervised Object Localization