Papers Semantic Segmentation
“Semantic Segmentation” 태그가 달린 논문 15,671편 · 필터 해제
Preference-Guided Adaptation for Open-Vocabulary Semantic Segmentation via Prompt Disagreement
Open-vocabulary semantic segmentation (OVSS) enables pixel-level prediction over arbitrary text-specified vocabularies and has shown strong generalization on common benchmarks. However, OVSS performance often degrades in…
Semantic SegmentationRGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation
In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassing abundant categories and high-quality annotations. RGBD20K possess…
Semantic SegmentationGTR: Gated Token Recurrence for Efficient Dense Prediction
Self-attention-based vision backbones perform well on dense prediction, but the quadratic computational cost of global softmax attention limits their efficiency as image resolution increases. We introduce Gated Token Rec…
Monocular Depth EstimationInstance SegmentationSemantic SegmentationPose EstimationSSC-Priors: Exploring Semantic and Visibility Priors to Boost Lidar Semantic Scene Completion
This paper investigates easy strategies to boost the performance of existing networks for lidar semantic scene completion (SSC) without requiring complex architectural redesigns. The fact is that, over the last years, SS…
Semantic SegmentationPoint CloudsPiPS: Post-Hoc Prototypical Explanations for Interpretable Semantic Segmentation
With the increasing deployment of deep neural networks in critical systems, such as medical diagnostics and autonomous vehicles, ensuring their interpretability is crucial to building trust in decision-making systems. In…
Semantic SegmentationImage ClassificationObject LocalizationAutonomous VehiclesNeuroTS-Net: Multi-Class Semantic Segmentation of Pediatric Brain Tumors in Multi-Modal MRI
Pediatric brain tumors are a leading cause of cancer-related mortality in children, and their small, rare, and often low-contrast subregions make accurate manual delineation challenging. Reliable automated segmentation i…
Semantic SegmentationChannel-Wise and Token-Aware Post-Training Quantization for Visual State Space Duality
State space models (SSMs), particularly Mamba, have emerged as efficient alternatives to attention-based architectures and have been extended to vision through ViM, VMamba, and Visual State Space Duality (VSSD). Yet the …
Semantic SegmentationInstance SegmentationObject DetectionEnhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask
Deformable convolution networks have recently become popular for many computer vision tasks, especially for semantic segmentation, because of their exceptional capabilities in dynamic spatial modeling. However, due to th…
Semantic SegmentationImage ClassificationEdge DetectionGeometry Without Coordinates: LiDAR Diffusion as a 3D Feature Bridge
Transferring the rich priors of large 2D foundation models to sparse 3D LiDAR remains challenging, as training native 3D foundation models at comparable scale is limited by data and annotation scarcity. We introduce a Li…
Semantic SegmentationCLFTv2: Efficient Camera-LiDAR Fusion for Semantic Segmentation via Hierarchical Feature Pyramids
Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CLFTv2, a hierarchical camera-LiDAR fusion framework replacing global V…
Semantic SegmentationAutonomous DrivingLearning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution
Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct a high-resolution hyperspectral image (HR-HSI) by combining the fine spatial details of a high-resolution multispectral image (HR-MSI) with the rich…
Spectral ReconstructionImage Super-ResolutionSemantic SegmentationBridging Modalities and Tasks: A Unified Hierarchical ViT for SAR-to-Optical Translation and Semantic Segmentation
Synthetic Aperture Radar (SAR) images have all-weather, day-and-night observation capabilities. However, compared with optical images, their speckle noise and non-intuitive scattering mechanism limit the interpretability…
Semantic SegmentationSELECT: SELEctive Context Transfer for Class-Incremental Semantic Segmentation
Class-Incremental Semantic Segmentation (CISS) is fundamentally challenged by catastrophic forgetting and background shift, where learning new concepts degrades performance on previously seen classes. While existing meth…
Semantic SegmentationMotion-Saliency Complementary Masked Modeling for Point Cloud Video Understanding
Point cloud video representation learning is crucial for 3D dynamic scene understanding. In this paper, we propose MoSaiC, a novel Motion-Saliency Complementary masked modeling framework for self-supervised point cloud v…
Representation LearningSemantic SegmentationScene UnderstandingAction SegmentationCedarCypress3D: an annotated UAV-LiDAR dataset of individual trees in planted cedar and cypress forests
Individual tree measurements derived from Light Detection and Ranging (LiDAR) mounted on Unmanned Aerial Vehicles (UAV) provide valuable information for forest inventory, ecosystem monitoring, and sustainable forest mana…
Instance SegmentationSemantic SegmentationPoint CloudsGAN-Based Semantic Communication for Image Transmission in IoV
For cooperative perception in the internet of vehicles, this paper proposes a generative adversarial network-based semantic communication framework to address the efficiency and fidelity bottlenecks of traditional commun…
Semantic CommunicationSemantic SegmentationImage ReconstructionDetection of Christmas tree plantations from high-resolution aerial imagery. A case study in the French Morvan
Christmas tree plantations are economically relevant, yet a largely unexplored application domain in Remote Sensing (RS). Their delineation is challenging because of high planting density, short rotation cycles, visual c…
Semantic SegmentationDINOcular: Self-Supervised Visuospatial Representations
We introduce a self-supervised framework for learning joint visuospatial representations from RGB-D observations. While modern vision foundation models are trained almost exclusively on RGB images, many embodied systems …
Semantic SegmentationText-to-seed generation: Training-free open-vocabulary seeded semantic segmentation via re-purposing diffusion as text-guided seed generator
Open-vocabulary semantic segmentation (OVSS) aims to segment image regions corresponding to arbitrary text queries. Although the Segment Anything Model (SAM) is a powerful foundation model for segmentation, its standalon…
Semantic SegmentationDifficulty-Aware Sample Allocation for Adaptive Data Augmentation in Semantic Segmentation
Data augmentation is a standard component of modern semantic segmentation pipelines, but most augmentation techniques allocate transformations uniformly across training samples or adapt to a single difficulty signal such…
Semantic SegmentationData Augmentation