Papers Unsupervised Saliency Detection
“Unsupervised Saliency Detection” 태그가 달린 논문 13편 · 필터 해제
Learning Accurate Segmentation Purely from Self-Supervision
Accurately segmenting objects without any manual annotations remains one of the core challenges in computer vision. In this work, we introduce Selfment, a fully self-supervised framework that segments foreground objects …
Unsupervised Saliency DetectionZero-shot GeneralizationObject DetectionTowards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context
Unsupervised salient object detection aims to detect salient objects without using supervision signals eliminating the tedious task of manually labeling salient objects. To improve training efficiency, end-to-end methods…
Contrastive Learningobject-detectionObject DetectionSaliency Detection+2Unsupervised Object Localization: Observing the Background to Discover Objects
Recent advances in self-supervised visual representation learning have paved the way for unsupervised methods tackling tasks such as object discovery and instance segmentation. However, discovering objects in an image wi…
Instance SegmentationObjectObject DiscoveryObject Localization+7MOVE: 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+7TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut
In this paper, we describe a graph-based algorithm that uses the features obtained by a self-supervised transformer to detect and segment salient objects in images and videos. With this approach, the image patches that c…
Object DiscoverySaliency DetectionSegmentationSemantic Segmentation+6Unsupervised Salient Object Detection with Spectral Cluster Voting
In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features. We make the following contributions: (i) We revisit spectral clu…
ClusteringObjectobject-detectionObject Detection+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+5Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection
Deep learning-based image salient object detection (SOD) heavily relies on large-scale training data with pixel-wise labeling. High-quality labels involve intensive labor and are expensive to acquire. In this paper, …
Deep Learningobject-detectionObject DetectionSaliency Detection+2Learning Background Invariance Improves Generalization and Robustness in Self-Supervised Learning on ImageNet and Beyond
Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training mode…
Data AugmentationSaliency DetectionSelf-Supervised LearningUnsupervised Saliency DetectionCharacterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations
Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training mode…
Contrastive LearningData AugmentationImage ClassificationRepresentation Learning+3Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization task…
Object DiscoverySaliency DetectionUnsupervised Saliency DetectionDeep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective
The success of current deep saliency detection methods heavily depends on the availability of large-scale supervision in the form of per-pixel labeling. Such supervision, while labor-intensive and not always possible, te…
BenchmarkingSaliency DetectionSaliency PredictionUnsupervised Saliency DetectionWeakly Supervised Salient Object Detection Using Image Labels
Deep learning based salient object detection has recently achieved great success with its performance greatly outperforms any other unsupervised methods. However, annotating per-pixel saliency masks is a tedious and inef…
Objectobject-detectionObject DetectionRGB Salient Object Detection+3