Papers Single-object discovery
“Single-object discovery” 태그가 달린 논문 10편 · 필터 해제
PEEKABOO: Hiding parts of an image for unsupervised object localization
Localizing objects in an unsupervised manner poses significant challenges due to the absence of key visual information such as the appearance, type and number of objects, as well as the lack of labeled object classes typ…
Objectobject-detectionObject DetectionObject Discovery+4MOVE: Unsupervised Movable Object Segmentation and Detection
We introduce MOVE, a novel method to segment objects without any form of supervision. MOVE exploits the fact that foreground objects can be shifted locally relative to their initial position and result in realistic (undi…
Class-agnostic Object DetectionObjectobject-detectionObject Detection+7K-means for unsupervised instance segmentation using a self-supervised transformer
Instance segmentation is a fundamental task in computer vision that assigns every pixel to an appropriate class and localizes objects into bounding boxes. However, collecting pixel-level segmentation labels is more reso…
Instance SegmentationObject DetectionObject DiscoverySegmentation+3Self-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+5Localizing 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 LocalizationLarge-Scale Unsupervised Object Discovery
Existing approaches to unsupervised object discovery (UOD) do not scale up to large datasets without approximations that compromise their performance. We propose a novel formulation of UOD as a ranking problem, amenable …
Multi-object discoveryObjectObject DiscoverySingle-object discoveryEmerging Properties in Self-Supervised Vision Transformers
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised metho…
Copy DetectionImage ClassificationImage RetrievalLinear evaluation+7Toward unsupervised, multi-object discovery in large-scale image collections
This paper addresses the problem of discovering the objects present in a collection of images without any supervision. We build on the optimization approach of Vo et al. (CVPR'19) with several key novelties: (1) We propo…
Multi-object colocalizationMulti-object discoveryObject DiscoveryRegion Proposal+2Unsupervised Image Matching and Object Discovery as Optimization
Learning with complete or partial supervision is powerful but relies on ever-growing human annotation efforts. As a way to mitigate this serious problem, as well as to serve specific applications, unsupervised learning h…
ObjectObject DiscoverySingle-object colocalizationSingle-object discoveryUnsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming
Reusable model design becomes desirable with the rapid expansion of computer vision and machine learning applications. In this paper, we focus on the reusability of pre-trained deep convolutional models. Specifically, di…
Objectobject-detectionObject DetectionObject Discovery+2