Papers One-Shot Object Detection
“One-Shot Object Detection” 태그가 달린 논문 20편 · 필터 해제
Learning Gaussian Data Augmentation in Feature Space for One-shot Object Detection in Manga
We tackle one-shot object detection in Japanese Manga. The rising global popularity of Japanese manga has made the object detection of character faces increasingly important, with potential applications such as automatic…
ColorizationData AugmentationObjectobject-detection+2Detect Everything with Few Examples
Few-shot object detection aims at detecting novel categories given only a few example images. It is a basic skill for a robot to perform tasks in open environments. Recent methods focus on finetuning strategies, with com…
Binary ClassificationCross-Domain Few-Shot Object DetectionFew-Shot Object DetectionObject+4Adaptive Base-class Suppression and Prior Guidance Network for One-Shot Object Detection
One-shot object detection (OSOD) aims to detect all object instances towards the given category specified by a query image. Most existing studies in OSOD endeavor to explore effective cross-image correlation and alleviat…
object-detectionObject DetectionOne-Shot Object DetectionOne-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
Active consumption of digital documents has yielded scope for research in various applications, including search. Traditionally, searching within a document has been cast as a text matching problem ignoring the rich layo…
document understandingobject-detectionObject DetectionOne-Shot Object Detection+2Identification of Binary Neutron Star Mergers in Gravitational-Wave Data Using YOLO One-Shot Object Detection
We demonstrate the application of the YOLOv5 model, a general purpose convolution-based single-shot object detection model, in the task of detecting binary neutron star (BNS) coalescence events from gravitational-wave da…
object-detectionObject DetectionOne-Shot Object DetectionSynthetic Data GenerationSimple Open-Vocabulary Object Detection with Vision Transformers
Combining simple architectures with large-scale pre-training has led to massive improvements in image classification. For object detection, pre-training and scaling approaches are less well established, especially in the…
Described Object Detectionimage-classificationImage ClassificationObject+4Semantic-aligned Fusion Transformer for One-shot Object Detection
One-shot object detection aims at detecting novel objects according to merely one given instance. With extreme data scarcity, current approaches explore various feature fusions to obtain directly transferable meta-knowle…
AttributeObjectobject-detectionObject Detection+1Balanced and Hierarchical Relation Learning for One-Shot Object Detection
Instance-level feature matching is significantly important to the success of modern one-shot object detectors. Recently, the methods based on the metric-learning paradigm have achieved an impressive process. Most of …
Metric Learningobject-detectionObject DetectionOne-Shot Object Detection+1A Survey of Deep Learning for Low-Shot Object Detection
Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the scenario where the annotated data is scarce…
Deep LearningFew-Shot LearningFew-Shot Object Detectionimage-classification+8Adaptive Image Transformer for One-Shot Object Detection
One-shot object detection tackles a challenging task that aims at identifying within a target image all object instances of the same class, implied by a query image patch. The main difficulty lies in the situation th…
DecoderObjectobject-detectionObject Detection+2CAT: Cross-Attention Transformer for One-Shot Object Detection
Given a query patch from a novel class, one-shot object detection aims to detect all instances of that class in a target image through the semantic similarity comparison. However, due to the extremely limited guidance in…
Objectobject-detectionObject DetectionOne-Shot Object Detection+2FOC OSOD: Focus on Classification One-Shot Object Detection
One-shot object detection (OSOD) aims at detecting all instances that are consistent with the category of the single reference image. OSOD achieves object detection by comparing the query image and the reference image. W…
ClassificationGeneral ClassificationObjectobject-detection+2A Broad Dataset is All You Need for One-Shot Object Detection
Is it possible to detect arbitrary objects from a single example? A central problem of all existing attempts at one-shot object detection is the generalization gap: Object categories used during training are detected muc…
AllFew-Shot LearningMetric LearningObject+3Quasi-Dense Similarity Learning for Multiple Object Tracking
Similarity learning has been recognized as a crucial step for object tracking. However, existing multiple object tracking methods only use sparse ground truth matching as the training objective, while ignoring the majori…
Contrastive LearningMetric LearningMulti-Object TrackingMultiple Object Tracking+4One-Shot Object Detection without Fine-Tuning
Deep learning has revolutionized object detection thanks to large-scale datasets, but their object categories are still arguably very limited. In this paper, we attempt to enrich such categories by addressing the one-sho…
Metric LearningObjectobject-detectionObject Detection+2OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features
In this paper, we consider the task of one-shot object detection, which consists in detecting objects defined by a single demonstration. Differently from the standard object detection, the classes of objects used for tra…
object-detectionObject DetectionOne-Shot Object DetectionOne-Shot Object Detection with Co-Attention and Co-Excitation
This paper aims to tackle the challenging problem of one-shot object detection. Given a query image patch whose class label is not included in the training data, the goal of the task is to detect all instances of the sam…
object-detectionObject DetectionOne-Shot Object DetectionRegion ProposalOne-Shot Instance Segmentation
We tackle the problem of one-shot instance segmentation: Given an example image of a novel, previously unknown object category, find and segment all objects of this category within a complex scene. To address this challe…
Few-Shot LearningFew-Shot Object DetectionInstance SegmentationObject Detection+4DroNet: Efficient convolutional neural network detector for real-time UAV applications
Unmanned Aerial Vehicles (drones) are emerging as a promising technology for both environmental and infrastructure monitoring, with broad use in a plethora of applications. Many such applications require the use of compu…
Object Detection In Aerial ImagesOne-Shot Object DetectionReal-Time Object Detectionvehicle detectionRepMet: Representative-based metric learning for classification and one-shot object detection
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few exam…
ClassificationFew-Shot Object DetectionGeneral ClassificationMetric Learning+4