Papers Superpixels
“Superpixels” 태그가 달린 논문 371편 · 필터 해제
YouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videos
3D semantic occupancy prediction in the past was considered to require precise geometric relationships in order to enable effective training. However, in complex indoor environments, the large-scale and widespread collec…
3D Semantic Occupancy PredictionRepresentation LearningSuperpixelsStructural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clustering
Hyperspectral image (HSI) clustering assigns similar pixels to the same class without any annotations, which is an important yet challenging task. For large-scale HSIs, most methods rely on superpixel segmentation and pe…
ClusteringContrastive Learninghyperspectral image clusteringImage Clustering+2Delving Deep into Semantic Relation Distillation
Knowledge distillation has become a cornerstone technique in deep learning, facilitating the transfer of knowledge from complex models to lightweight counterparts. Traditional distillation approaches focus on transferrin…
Knowledge DistillationModel CompressionRelationSuperpixelsForestSplats: Deformable transient field for Gaussian Splatting in the Wild
Recently, 3D Gaussian Splatting (3D-GS) has emerged, showing real-time rendering speeds and high-quality results in static scenes. Although 3D-GS shows effectiveness in static scenes, their performance significantly degr…
SuperpixelsUSegMix: Unsupervised Segment Mix for Efficient Data Augmentation in Pathology Images
In computational pathology, researchers often face challenges due to the scarcity of labeled pathology datasets. Data augmentation emerges as a crucial technique to mitigate this limitation. In this study, we introduce a…
Cancer ClassificationData AugmentationSuperpixelsLargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Recent advancements in vision foundation models (VFMs) have revolutionized visual perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous driving applications, remains underexplored. …
Autonomous DrivingContrastive Learningobject-detectionObject Detection+3Superpixel Tokenization for Vision Transformers: Preserving Semantic Integrity in Visual Tokens
Transformers, a groundbreaking architecture proposed for Natural Language Processing (NLP), have also achieved remarkable success in Computer Vision. A cornerstone of their success lies in the attention mechanism, which …
SuperpixelsAlphaTablets: A Generic Plane Representation for 3D Planar Reconstruction from Monocular Videos
We introduce AlphaTablets, a novel and generic representation of 3D planes that features continuous 3D surface and precise boundary delineation. By representing 3D planes as rectangles with alpha channels, AlphaTablets c…
SuperpixelsSuperpixel Cost Volume Excitation for Stereo Matching
In this work, we concentrate on exciting the intrinsic local consistency of stereo matching through the incorporation of superpixel soft constraints, with the objective of mitigating inaccuracies at the boundaries of pre…
Stereo MatchingSuperpixelsSP${ }^3$ : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation
Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation datasets. Weakly semi-supervised medical i…
Image SegmentationMedical Image SegmentationOrgan SegmentationPseudo Label+4Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data
Recently, implicit neural representations (INRs) have attracted increasing attention for multi-dimensional data recovery. However, INRs simply map coordinates via a multi-layer perception (MLP) to corresponding values, i…
SuperpixelsQuantum Information-Empowered Graph Neural Network for Hyperspectral Change Detection
Change detection (CD) is a critical remote sensing technique for identifying changes in the Earth's surface over time. The outstanding substance identifiability of hyperspectral images (HSIs) has significantly enhanced t…
Change DetectionGraph Neural NetworkSuperpixelsSuperpixel Segmentation: A Long-Lasting Ill-Posed Problem
For many years, image over-segmentation into superpixels has been essential to computer vision pipelines, by creating homogeneous and identifiable regions of similar sizes. Such constrained segmentation problem would req…
SegmentationSemantic SegmentationSuperpixelsSTA-Unet: Rethink the semantic redundant for Medical Imaging Segmentation
In recent years, significant progress has been made in the medical image analysis domain using convolutional neural networks (CNNs). In particular, deep neural networks based on a U-shaped architecture (UNet) with skip c…
Medical Image AnalysisMedical Image SegmentationOrgan SegmentationSuperpixelsA comprehensive review and new taxonomy on superpixel segmentation
Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications since it allows for reducing the workl…
SuperpixelsA novel application of Shapley values for large multidimensional time-series data: Applying explainable AI to a DNA profile classification neural network
The application of Shapley values to high-dimensional, time-series-like data is computationally challenging - and sometimes impossible. For $N$ inputs the problem is $2^N$ hard. In image processing, clusters of pixels, r…
ClassificationSuperpixelsTime SeriesHow to Identify Good Superpixels for Deforestation Detection on Tropical Rainforests
The conservation of tropical forests is a topic of significant social and ecological relevance due to their crucial role in the global ecosystem. Unfortunately, deforestation and degradation impact millions of hectares a…
SuperpixelsLagrangian Motion Fields for Long-term Motion Generation
Long-term motion generation is a challenging task that requires producing coherent and realistic sequences over extended durations. Current methods primarily rely on framewise motion representations, which capture only s…
Motion GenerationSuperpixelsFrom Pixels to Objects: A Hierarchical Approach for Part and Object Segmentation Using Local and Global Aggregation
In this paper, we introduce a hierarchical transformer-based model designed for sophisticated image segmentation tasks, effectively bridging the granularity of part segmentation with the comprehensive scope of object seg…
Computational EfficiencyImage SegmentationObjectSegmentation+2ESA: Annotation-Efficient Active Learning for Semantic Segmentation
Active learning enhances annotation efficiency by selecting the most revealing samples for labeling, thereby reducing reliance on extensive human input. Previous methods in semantic segmentation have centered on individu…
Active LearningSemantic SegmentationSuperpixels