Papers Unsupervised Semantic Segmentation
“Unsupervised Semantic Segmentation” 태그가 달린 논문 101편 · 필터 해제
UMSS: Towards Unsupervised Multi-modal Semantic Segmentation
Multimodal semantic segmentation (MSS) is essential for robust perception in complex environments, yet its potential remains largely untapped because of the prohibitive cost of human annotations. While unsupervised seman…
Unsupervised Semantic SegmentationUnsupervised Semantic Segmentation Facilitates Model Understanding
Self-supervised learning (SSL) has produced a diverse landscape of vision transformers (ViTs) whose pretrained representations support a wide range of downstream tasks. Towards a better understanding of these models, a b…
Unsupervised Semantic SegmentationSelf-Supervised LearningContrastive LearningP-SLCR: Unsupervised Point Cloud Semantic Segmentation via Prototypes Structure Learning and Consistent Reasoning
Current semantic segmentation approaches for point cloud scenes heavily rely on manual labeling, while research on unsupervised semantic segmentation methods specifically for raw point clouds is still in its early stages…
Unsupervised Semantic SegmentationPoint CloudsUnsupervised Semantic Segmentation in Synchrotron Computed Tomography with Self-Correcting Pseudo Labels
X-ray computed tomography (CT) is a widely used imaging technique that provides detailed examinations into the internal structure of an object with synchrotron CT (SR-CT) enabling improved data quality by using higher en…
Unsupervised Semantic SegmentationDynaGuide: A Generalizable Dynamic Guidance Framework for Unsupervised Semantic Segmentation
Unsupervised image segmentation is a critical task in computer vision. It enables dense scene understanding without human annotations, which is especially valuable in domains where labelled data is scarce. However, exist…
Unsupervised Semantic SegmentationScene UnderstandingImage SegmentationDiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF
3D semantic segmentation provides high-level scene understanding for applications in robotics, autonomous systems, \textit{etc}. Traditional methods adapt exclusively to either task-specific goals (open-vocabulary segmen…
Unsupervised Semantic Segmentation3D Semantic SegmentationScene UnderstandingUrban1960SatSeg: Unsupervised Semantic Segmentation of Mid-20$^{th}$ century Urban Landscapes with Satellite Imageries
Historical satellite imagery, such as mid-20$^{th}$ century Keyhole data, offers rare insights into understanding early urban development and long-term transformation. However, severe quality degradation (e.g., distortio…
SegmentationSelf-Supervised LearningSemantic SegmentationUnsupervised Semantic SegmentationLogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds
We study the problem of unsupervised 3D semantic segmentation on raw point clouds without needing human labels in training. Existing methods usually formulate this problem into learning per-point local features followed …
3D Semantic SegmentationSegmentationSemantic SegmentationUnsupervised 3D Semantic Segmentation+1Federated Unsupervised Semantic Segmentation
This work explores the application of Federated Learning (FL) in Unsupervised Semantic image Segmentation (USS). Recent USS methods extract pixel-level features using frozen visual foundation models and refine them throu…
Federated LearningImage SegmentationSegmentationSemantic Segmentation+1Hierarchical Context Learning of object components for unsupervised semantic segmentation
Unsupervised Semantic Segmentation (USS) aims to learn semantically rich and dense representations without relying on labels. Recent advances in self-supervised learning have demonstrated the potential of pretrained vi…
ObjectSelf-Supervised LearningSemantic SegmentationUnsupervised Semantic SegmentationScene-Centric Unsupervised Panoptic Segmentation
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In contrast to prior work on unsupervised pano…
Instance SegmentationPanoptic SegmentationPseudo LabelScene Understanding+5COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation
Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical research. While unsupervised CIS (UCIS) models …
Cell SegmentationInstance SegmentationSemantic SegmentationUnsupervised Semantic SegmentationNPSim: Nighttime Photorealistic Simulation From Daytime Images With Monocular Inverse Rendering and Ray Tracing
Semantic segmentation is an important task for autonomous driving. A powerful autonomous driving system should be capable of handling images under all conditions, including nighttime. Generating accurate and diverse nigh…
Autonomous DrivingInverse RenderingSemantic SegmentationUnsupervised Semantic SegmentationGraPix: Exploring Graph Modularity Optimization for Unsupervised Pixel Clustering
A vision transformer learns high-quality patch embeddings during the self-supervised training, which plays a crucial role in many unsupervised downstream tasks like object localization, object detection, and sparse seman…
AttributeClusteringObject LocalizationSegmentation+2ULTra: Unveiling Latent Token Interpretability in Transformer Based Understanding
Transformers have revolutionized Computer Vision (CV) and Natural Language Processing (NLP) through self-attention mechanisms. However, due to their complexity, their latent token representations are often difficult to i…
SegmentationSemantic SegmentationText SummarizationUnsupervised Semantic SegmentationUnsupervised semantic segmentation of urban high-density multispectral point clouds
The availability of highly accurate urban airborne laser scanning (ALS) data will increase rapidly in the future, especially as acquisition costs decrease, for example through the use of drones. Current challenges in dat…
Deep ClusteringSemantic SegmentationUnsupervised Semantic SegmentationCUS3D :CLIP-based Unsupervised 3D Segmentation via Object-level Denoise
To ease the difficulty of acquiring annotation labels in 3D data, a common method is using unsupervised and open-vocabulary semantic segmentation, which leverage 2D CLIP semantic knowledge. In this paper, unlike previous…
Open Vocabulary Semantic SegmentationOpen-Vocabulary Semantic SegmentationSegmentationSemantic Segmentation+1Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features
We introduce Segmentation by Factorization (F-SEG), an unsupervised segmentation method for pathology that generates segmentation masks from pre-trained deep learning models. F-SEG allows the use of pre-trained deep neur…
Deep LearningSegmentationSemantic SegmentationUnsupervised Semantic SegmentationiSeg: An Iterative Refinement-based Framework for Training-free Segmentation
Stable diffusion has demonstrated strong image synthesis ability to given text descriptions, suggesting it to contain strong semantic clue for grouping objects. The researchers have explored employing stable diffusion fo…
Image GenerationSegmentationSemantic SegmentationUnsupervised Semantic SegmentationReCLIP++: Learn 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 understa…
SegmentationSemantic SegmentationUnsupervised Semantic Segmentation