Papers Thermal Image Segmentation
“Thermal Image Segmentation” 태그가 달린 논문 84편 · 필터 해제
Boosting Cross-spectral Unsupervised Domain Adaptation for Thermal Semantic Segmentation
In autonomous driving, thermal image semantic segmentation has emerged as a critical research area, owing to its ability to provide robust scene understanding under adverse visual conditions. In particular, unsupervised …
Autonomous DrivingDomain AdaptationImage SegmentationScene Understanding+4Unveiling the Potential of Segment Anything Model 2 for RGB-Thermal Semantic Segmentation with Language Guidance
The perception capability of robotic systems relies on the richness of the dataset. Although Segment Anything Model 2 (SAM2), trained on large datasets, demonstrates strong perception potential in perception tasks, its i…
DecoderSemantic SegmentationThermal Image SegmentationStitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation
Multimodal semantic segmentation shows significant potential for enhancing segmentation accuracy in complex scenes. However, current methods often incorporate specialized feature fusion modules tailored to specific modal…
SegmentationSemantic SegmentationThermal Image SegmentationRoadFormer+: Delivering RGB-X Scene Parsing through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion
Task-specific data-fusion networks have marked considerable achievements in urban scene parsing. Among these networks, our recently proposed RoadFormer successfully extracts heterogeneous features from RGB images and sur…
Scene ParsingSemantic SegmentationThermal Image SegmentationCSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving Scenes
Semantic segmentation, as a crucial component of complex visual interpretation, plays a fundamental role in autonomous vehicle vision systems. Recent studies have significantly improved the accuracy of semantic segmentat…
Autonomous VehiclesImage SegmentationReal-Time Semantic SegmentationRGBD Semantic Segmentation+4UniRGB-IR: A Unified Framework for RGB-Infrared Semantic Tasks via Adapter Tuning
Semantic analysis on visible (RGB) and infrared (IR) images has gained attention for its ability to be more accurate and robust under low-illumination and complex weather conditions. Due to the lack of pre-trained founda…
Multispectral Object DetectionPedestrian DetectionThermal Image SegmentationSigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation
Multi-modal semantic segmentation significantly enhances AI agents' perception and scene understanding, especially under adverse conditions like low-light or overexposed environments. Leveraging additional modalities (X-…
DecoderMambaScene UnderstandingSegmentation+3HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
Data-fusion networks have shown significant promise for RGB-thermal scene parsing. However, the majority of existing studies have relied on symmetric duplex encoders for heterogeneous feature extraction and fusion, payin…
Scene ParsingSemantic SegmentationThermal Image SegmentationContext-Aware Interaction Network for RGB-T Semantic Segmentation
RGB-T semantic segmentation is a key technique for autonomous driving scenes understanding. For the existing RGB-T semantic segmentation methods, however, the effective exploration of the complementary relationship betwe…
Autonomous DrivingSemantic SegmentationThermal Image SegmentationIGFNet: Illumination-Guided Fusion Network for Semantic Scene Understanding using RGB-Thermal Images
Semantic scene understanding is a fundamental task for autonomous driving. It serves as a build block for many downstream tasks. Under challenging illumination conditions, thermal images can provide complementary informa…
Autonomous DrivingScene UnderstandingThermal Image SegmentationEfficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Multimodal (e.g., RGB-Depth/RGB-Thermal) fusion has shown great potential for improving semantic segmentation in complex scenes (e.g., indoor/low-light conditions). Existing approaches often fully fine-tune a dual-branch…
Decoderobject-detectionObject DetectionPrompt Learning+6InfraParis: A multi-modal and multi-task autonomous driving dataset
Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Consequently, these models struggle to handl…
Autonomous DrivingMonocular Depth EstimationObject DetectionSemantic Segmentation+2CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing
Color–thermal (RGB-T) urban scene parsing has recently attracted widespread interest. However, most existing approaches to RGB-T urban scene parsing do not deeply explore the information complementarity between RGB-T fea…
Scene ParsingThermal Image SegmentationMMSFormer: Multimodal Transformer for Material and Semantic Segmentation
Leveraging information across diverse modalities is known to enhance performance on multimodal segmentation tasks. However, effectively fusing information from different modalities remains challenging due to the unique c…
SegmentationSemantic SegmentationThermal Image SegmentationChannel and Spatial Relation-Propagation Network for RGB-Thermal Semantic Segmentation
RGB-Thermal (RGB-T) semantic segmentation has shown great potential in handling low-light conditions where RGB-based segmentation is hindered by poor RGB imaging quality. The key to RGB-T semantic segmentation is to effe…
RelationSegmentationSemantic SegmentationThermal Image SegmentationEGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene Parsing
Urban scene parsing is the core of the intelligent transportation system, and RGB–thermal urban scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing ap…
Scene ParsingSemantic SegmentationThermal Image SegmentationPAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
Infrared and visible image fusion is a powerful technique that combines complementary information from different modalities for downstream semantic perception tasks. Existing learning-based methods show remarkable perfor…
Infrared And Visible Image FusionSegmentationSemantic SegmentationThermal Image SegmentationMulti-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation. Early efforts focus on boosting the performance for only one task, \emph{e.g.,} fusion or segmentation, making i…
Autonomous DrivingSegmentationSemantic SegmentationThermal Image SegmentationVariational Probabilistic Fusion Network for RGB-T Semantic Segmentation
RGB-T semantic segmentation has been widely adopted to handle hard scenes with poor lighting conditions by fusing different modality features of RGB and thermal images. Existing methods try to find an optimal fusion feat…
SegmentationSemantic SegmentationThermal Image SegmentationResidual Spatial Fusion Network for RGB-Thermal Semantic Segmentation
Semantic segmentation plays an important role in widespread applications such as autonomous driving and robotic sensing. Traditional methods mostly use RGB images which are heavily affected by lighting conditions, \eg, d…
Autonomous DrivingSaliency DetectionSegmentationSemantic Segmentation+1