Papers Weakly-Supervised Object Localization
“Weakly-Supervised Object Localization” 태그가 달린 논문 140편 · 필터 해제
Combinational Class Activation Maps for Weakly Supervised Object Localization
Weakly supervised object localization has recently attracted attention since it aims to identify both class labels and locations of objects by using image-level labels. Most previous methods utilize the activation map co…
ObjectObject LocalizationWeakly-Supervised Object LocalizationAdaptively Denoising Proposal Collection for Weakly Supervised Object Localization
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations. The proposed approach is built on the observation t…
DenoisingMultiple Instance LearningObjectObject Localization+1Information Entropy Based Feature Pooling for Convolutional Neural Networks
In convolutional neural networks (CNNs), we propose to estimate the importance of a feature vector at a spatial location in the feature maps by the network's uncertainty on its class prediction, which can be quantified u…
General ClassificationObject LocalizationSegmentationSemantic Segmentation+3DANet: Divergent Activation for Weakly Supervised Object Localization
Weakly supervised object localization remains a challenge when learning object localization models from image category labels. Optimizing image classification tends to activate object parts and ignore the full object ext…
ClassificationGeneral Classificationimage-classificationImage Classification+3Multi-scale discriminative Region Discovery for Weakly-Supervised Object Localization
Localizing objects with weak supervision in an image is a key problem of the research in computer vision community. Many existing Weakly-Supervised Object Localization (WSOL) approaches tackle this problem by estimating …
Object LocalizationWeakly-Supervised Object LocalizationDual-attention Focused Module for Weakly Supervised Object Localization
The research on recognizing the most discriminative regions provides referential information for weakly supervised object localization with only image-level annotations. However, the most discriminative regions usually c…
ObjectObject LocalizationObject RecognitionPosition+1Weakly Supervised Localization Using Background Images
Weakly Supervised Object Localization (WSOL) methodsusually rely on fully convolutional networks in order to ob-tain class activation maps(CAMs) of targeted labels. How-ever, these networks always highlight the most disc…
ObjectObject LocalizationWeakly-Supervised Object LocalizationDeep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
Using deep learning models to diagnose cancer from histology data presents several challenges. Cancer grading and localization of regions of interest (ROIs) in these images normally relies on both image- and pixel-level …
General ClassificationModel SelectionObject LocalizationWeakly-supervised Learning+1Attention-based Dropout Layer for Weakly Supervised Object Localization
Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations. A common limitation for these techniques is that they cover only the most dis…
ObjectObject LocalizationWeakly-Supervised Object LocalizationMin-max Entropy for Weakly Supervised Pointwise Localization
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain. In this work, we focus on …
Object LocalizationWeakly-Supervised Object LocalizationLearning Instance Activation Maps for Weakly Supervised Instance Segmentation
Discriminative region responses residing inside an object instance can be extracted from networks trained with image-level label supervision. However, learning the full extent of pixel-level instance response in a weakly…
Image-level Supervised Instance SegmentationInstance SegmentationObjectobject-detection+7Compression and Localization in Reinforcement Learning for ATARI Games
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed. While compressing deep neural networks has of late assumed great …
Atari GamesModel CompressionObject Localizationreinforcement-learning+3C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors. Many WSOD approaches adopt multipl…
Multiple Instance LearningObjectobject-detectionObject Detection+3Modularized Textual Grounding for Counterfactual Resilience
Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries. To achieve high grounding precision, current textual grounding meth…
AttributecounterfactualNatural Language Visual GroundingPhrase Grounding+2Min-Entropy Latent Model for Weakly Supervised Object Detection
Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors. The inconsistency between the weak su…
image-classificationImage ClassificationObjectobject-detection+4Weakly Supervised Convolutional LSTM Approach for Tool Tracking in Laparoscopic Videos
Purpose: Real-time surgical tool tracking is a core component of the future intelligent operating room (OR), because it is highly instrumental to analyze and understand the surgical activities. Current methods for surgic…
Instrument RecognitionObject DetectionSurgical tool detectionVideo Object Tracking+1ML-LocNet: Improving Object Localization with Multi-view Learning Network
This paper addresses Weakly Supervised Object Localization (WSOL) with only image-level supervision. We propose a Multi-view Learning Localization Network (ML-LocNet) by incorporating multi-view learning into a two-phase…
MULTI-VIEW LEARNINGObject LocalizationWeakly-Supervised Object LocalizationSelf-produced Guidance for Weakly-supervised Object Localization
Weakly supervised methods usually generate localization results based on attention maps produced by classification networks. However, the attention maps exhibit the most discriminative parts of the object which are small…
ClassificationGeneral ClassificationObjectObject Localization+1Adversarial Complementary Learning for Weakly Supervised Object Localization
In this work, we propose Adversarial Complementary Learning (ACoL) to automatically localize integral objects of semantic interest with weak supervision. We first mathematically prove that class localization maps can be …
General ClassificationObjectObject LocalizationWeakly-Supervised Object LocalizationWeakly Supervised Object Localization on grocery shelves using simple FCN and Synthetic Dataset
We propose a weakly supervised method using two algorithms to predict object bounding boxes given only an image classification dataset. First algorithm is a simple Fully Convolutional Network (FCN) trained to classify ob…
Decoderimage-classificationImage ClassificationObject+5