Papers Object Localization
“Object Localization” 태그가 달린 논문 726편 · 필터 해제
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
This paper studies the problem of learning semantic segmentation from image-level supervision only. Current popular solutions leverage object localization maps from classifiers as supervision signals, and struggle to mak…
ObjectObject LocalizationSegmentationSemantic Segmentation+2Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates
Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train m…
Deep AttentionImage SegmentationMedical Image SegmentationMulti-Task Learning+4MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. The MoNet3D algorithm is a novel and effective framework that can predict the 3D position of each object in a monocu…
Monocular 3D Object LocalizationObjectobject-detectionObject Detection+2Boundary Regularized Building Footprint Extraction From Satellite Images Using Deep Neural Network
In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to support a wide range of civil, public and military applications. One of the key informati…
Objectobject-detectionObject DetectionObject LocalizationBOUNDARY REGULARIZED BUILDING FOOTPRINT EXTRACTION FROM SATELLITE IMAGES USING DEEP NEURAL NETWORKS
In recent years, an ever-increasing number of remote satellites are orbiting the Earth which streams vast amount of visual data to support a wide range of civil, public and military applications. One of the key informat…
Objectobject-detectionObject DetectionObject 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+3Offset Bin Classification Network for Accurate Object Detection
Object detection combines object classification and object localization problems. Most existing object detection methods usually locate objects by leveraging regression networks trained with Smooth L1 loss function to pr…
ClassificationGeneral ClassificationObjectobject-detection+3Salient Object Detection Combining a Self-attention Module and a Feature Pyramid Network
Salient object detection has achieved great improvement by using the Fully Convolution Network (FCN). However, the FCN-based U-shape architecture may cause the dilution problem in the high-level semantic information duri…
Objectobject-detectionObject DetectionObject Localization+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+4Spatio-temporal Tubelet Feature Aggregation and Object Linking in Videos
This paper addresses the problem of how to exploit spatio-temporal information available in videos to improve the object detection precision. We propose a two stage object detector called FANet based on short-term spatio…
Objectobject-detectionObject DetectionObject Localization+1How spatial frequencies and color drive object search in real-world scenes: A new eye-movement corpus
When studying how people search for objects in scenes, the inhomogeneity of the visual field is often ignored. Due to physiological limitations peripheral vision is blurred and mainly uses coarse-grained information (i.e…
ObjectObject LocalizationPairwise 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 LocalizationRethinking Object Detection in Retail Stores
The convention standard for object detection uses a bounding box to represent each individual object instance. However, it is not practical in the industry-relevant applications in the context of warehouses due to severe…
Objectobject-detectionObject DetectionObject LocalizationGround Truth Evaluation of Neural Network Explanations with CLEVR-XAI
The rise of deep learning in today's applications entailed an increasing need in explaining the model's decisions beyond prediction performances in order to foster trust and accountability. Recently, the field of explain…
BenchmarkingExplainable Artificial Intelligence (XAI)Feature ImportanceObject Localization+3StarNet: towards Weakly Supervised Few-Shot Object Detection
Few-shot detection and classification have advanced significantly in recent years. Yet, detection approaches require strong annotation (bounding boxes) both for pre-training and for adaptation to novel classes, and class…
ClassificationFew-Shot LearningFew-Shot Object DetectionGeneral Classification+4PBRnet: Pyramidal Bounding Box Refinement to Improve Object Localization Accuracy
Many recently developed object detectors focused on coarse-to-fine framework which contains several stages that classify and regress proposals from coarse-grain to fine-grain, and obtains more accurate detection graduall…
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 LocalizationWho Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference
A significant amount of people die in road accidents due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in an urgent need. Risky situations…
Causal InferenceObjectObject 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+1