Papers Unsupervised Semantic Segmentation
“Unsupervised Semantic Segmentation” 태그가 달린 논문 101편 · 필터 해제
Progressive Proxy Anchor Propagation for Unsupervised Semantic Segmentation
The labor-intensive labeling for semantic segmentation has spurred the emergence of Unsupervised Semantic Segmentation. Recent studies utilize patch-wise contrastive learning based on features from image-level self-super…
Contrastive LearningSegmentationSemantic SegmentationUnsupervised Semantic SegmentationDeep ContourFlow: Advancing Active Contours with Deep Learning
This paper introduces a novel approach that combines unsupervised active contour models with deep learning for robust and adaptive image segmentation. Indeed, traditional active contours, provide a flexible framework for…
Deep LearningImage SegmentationOne-Shot SegmentationSegmentation+2DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks. While prior works have addressed unsupervised image segmentation, they significantly lag behind su…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image Segmentation+3DynaSeg: A Deep Dynamic Fusion Method for Unsupervised Image Segmentation Incorporating Feature Similarity and Spatial Continuity
Our work tackles the fundamental challenge of image segmentation in computer vision, which is crucial for diverse applications. While supervised methods demonstrate proficiency, their reliance on extensive pixel-level an…
Image SegmentationSegmentationSemantic SegmentationUnsupervised Image Segmentation+1Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals
Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global semantic categories within an image corpus without any form of annotation. Building upo…
Representation LearningSegmentationSemantic SegmentationUnsupervised Semantic SegmentationOMH: Structured Sparsity via Optimally Matched Hierarchy for Unsupervised Semantic Segmentation
Unsupervised Semantic Segmentation (USS) involves segmenting images without relying on predefined labels, aiming to alleviate the burden of extensive human labeling. Existing methods utilize features generated by self-su…
ClusteringSemantic SegmentationUnsupervised Semantic SegmentationEAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
Semantic segmentation has innately relied on extensive pixel-level annotated data, leading to the emergence of unsupervised methodologies. Among them, leveraging self-supervised Vision Transformers for unsupervised seman…
ObjectRepresentation LearningSegmentationSemantic Segmentation+3Applying Unsupervised Semantic Segmentation to High-Resolution UAV Imagery for Enhanced Road Scene Parsing
There are two challenges presented in parsing road scenes from UAV images: the complexity of processing high-resolution images and the dependency on extensive manual annotations required by traditional supervised deep le…
Language ModellingRepresentation LearningScene ParsingSelf-Supervised Learning+2DatUS^2: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer
Successive proposals of several self-supervised training schemes continue to emerge, taking one step closer to developing a universal foundation model. In this process, the unsupervised downstream tasks are recognized as…
SegmentationSemantic SegmentationUnsupervised Semantic SegmentationSNIDA: Unlocking Few-Shot Object Detection with Non-linear Semantic Decoupling Augmentation
Once only a few-shot annotated samples are available the performance of learning-based object detection would be heavily dropped. Many few-shot object detection (FSOD) methods have been proposed to tackle this issue …
Data AugmentationDiversityFew-Shot Object DetectionObject+5Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation
Recent works utilize CLIP to perform the challenging unsupervised semantic segmentation task where only images without annotations are available. However we observe that when adopting CLIP to such a pixel-level under…
SegmentationSemantic SegmentationUnsupervised Semantic SegmentationUnsupervised Universal Image Segmentation
Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle either semantic segmentation (e.g., STEGO) …
Image SegmentationInstance SegmentationPanoptic SegmentationSegmentation+8Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization
Unsupervised semantic segmentation (USS) aims to discover and recognize meaningful categories without any labels. For a successful USS, two key abilities are required: 1) information compression and 2) clustering capabil…
ClusteringDimensionality ReductionQuantizationSegmentation+2A Lightweight Clustering Framework for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation aims to categorize each pixel in an image into a corresponding class without the use of annotated data. It is a widely researched area as obtaining labeled datasets is expensive. While …
ClusteringSegmentationSemantic SegmentationUnsupervised Semantic SegmentationUnderstanding Self-Supervised Features for Learning Unsupervised Instance Segmentation
Self-supervised learning (SSL) can be used to solve complex visual tasks without human labels. Self-supervised representations encode useful semantic information about images, and as a result, they have already been used…
Instance SegmentationSegmentationSelf-Supervised LearningSemantic Segmentation+3Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation
Overhead line inspection greatly benefits from defect recognition using visible light imagery. Addressing the limitations of existing feature extraction techniques and the heavy data dependency of deep learning approache…
SegmentationSemantic SegmentationUnsupervised Semantic SegmentationSmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation. Prior works have primarily focused on leveraging prior knowledge of semantic consistency or pr…
SegmentationSelf-Supervised LearningSemantic SegmentationUnsupervised Semantic SegmentationPixel-Level Clustering Network for Unsupervised Image Segmentation
While image segmentation is crucial in various computer vision applications, such as autonomous driving, grasping, and robot navigation, annotating all objects at the pixel-level for training is nearly impossible. Theref…
Autonomous DrivingClusteringImage ReconstructionImage Segmentation+6Causal Unsupervised Semantic Segmentation
Unsupervised semantic segmentation aims to achieve high-quality semantic grouping without human-labeled annotations. With the advent of self-supervised pre-training, various frameworks utilize the pre-trained features to…
Causal InferencePredictionSegmentationSelf-Supervised Learning+2Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learning have made significant progress on this…
Feature CorrelationSemantic SegmentationUnsupervised Panoptic SegmentationUnsupervised Semantic Segmentation