Papers Zero-Shot Semantic Segmentation
“Zero-Shot Semantic Segmentation” 태그가 달린 논문 67편 · 필터 해제
Prototype-Anchored Generalized Manifold Regression for Unknown-Domain Object Detection
In this paper, we study Single-Domain Generalized Object Detection (Single-DGOD), which aims to transfer a detector trained on a single source domain to multiple unseen domains. Existing methods mainly rely on simulation…
Zero-Shot Semantic SegmentationDomain GeneralizationData AugmentationObject DetectionLangHOPS: Language Grounded Hierarchical Open-Vocabulary Part Segmentation
We propose LangHOPS, the first Multimodal Large Language Model (MLLM) based framework for open-vocabulary object-part instance segmentation. Given an image, LangHOPS can jointly detect and segment hierarchical object and…
Zero-Shot Semantic SegmentationInstance SegmentationSemantic4Safety: Causal Insights from Zero-shot Street View Imagery Segmentation for Urban Road Safety
Street-view imagery (SVI) offers a fine-grained lens on traffic risk, yet two fundamental challenges persist: (1) how to construct street-level indicators that capture accident-related features, and (2) how to quantify t…
Zero-Shot Semantic SegmentationCausal InferenceSolar PV Installation Potential Assessment on Building Facades Based on Vision and Language Foundation Models
Building facades represent a significant untapped resource for solar energy generation in dense urban environments, yet assessing their photovoltaic (PV) potential remains challenging due to complex geometries and semant…
Zero-Shot Semantic SegmentationSpatial ReasoningGeneralized Zero-Shot Learning for Point Cloud Segmentation with Evidence-Based Dynamic Calibration
Generalized zero-shot semantic segmentation of 3D point clouds aims to classify each point into both seen and unseen classes. A significant challenge with these models is their tendency to make biased predictions, often …
Zero-Shot Semantic SegmentationGeneralized Zero-Shot LearningPoint Cloud SegmentationPoint CloudsOmniMap: A General Mapping Framework Integrating Optics, Geometry, and Semantics
Robotic systems demand accurate and comprehensive 3D environment perception, requiring simultaneous capture of photo-realistic appearance (optical), precise layout shape (geometric), and open-vocabulary scene understandi…
Zero-Shot Semantic SegmentationScene UnderstandingZero Shot Domain Adaptive Semantic Segmentation by Synthetic Data Generation and Progressive Adaptation
Deep learning-based semantic segmentation models achieve impressive results yet remain limited in handling distribution shifts between training and test data. In this paper, we present SDGPA (Synthetic Data Generation an…
Zero-Shot Semantic SegmentationSynthetic Data GenerationSplit Matching for Inductive Zero-shot Semantic Segmentation
Zero-shot Semantic Segmentation (ZSS) aims to segment categories that are not annotated during training. While fine-tuning vision-language models has achieved promising results, these models often overfit to seen categor…
Object LocalizationSemantic SegmentationZero-Shot Semantic Segmentation3D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap
Existing zero-shot 3D point cloud segmentation methods often struggle with limited transferability from seen classes to unseen classes and from semantic to visual space. To alleviate this, we introduce 3D-PointZshotS, a …
Point Cloud SegmentationSemantic SegmentationTransfer LearningZero Shot Segmentation+1Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation
Generalized zero-shot semantic segmentation (GZS3) aims to achieve the human-level capability of segmenting not only seen classes but also novel class regions unseen in the training data through introducing the bridge of…
Graph MatchingSemantic SegmentationTransfer LearningZero-Shot Semantic SegmentationDisentangling CLIP for Multi-Object Perception
Vision-language models like CLIP excel at recognizing the single, prominent object in a scene. However, they struggle in complex scenes containing multiple objects. We identify a fundamental reason behind this limitation…
DisentanglementImage ClassificationObjectSemantic Segmentation+1FLAIR: VLM with Fine-grained Language-informed Image Representations
CLIP has shown impressive results in aligning images and texts at scale. However, its ability to capture detailed visual features remains limited because CLIP matches images and texts at a global level. To address this i…
Language ModelingLanguage ModellingRetrievalSemantic Segmentation+1Open-RGBT: Open-vocabulary RGB-T Zero-shot Semantic Segmentation in Open-world Environments
Semantic segmentation is a critical technique for effective scene understanding. Traditional RGB-T semantic segmentation models often struggle to generalize across diverse scenarios due to their reliance on pretrained mo…
Open Vocabulary Semantic SegmentationOpen-Vocabulary Semantic SegmentationScene UnderstandingSegmentation+3Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO
Grounding DINO and the Segment Anything Model (SAM) have achieved impressive performance in zero-shot object detection and image segmentation, respectively. Together, they have a great potential to revolutionize applicat…
Image SegmentationMedical Image Segmentationobject-detectionObject Detection+6OpenObj: Open-Vocabulary Object-Level Neural Radiance Fields with Fine-Grained Understanding
In recent years, there has been a surge of interest in open-vocabulary 3D scene reconstruction facilitated by visual language models (VLMs), which showcase remarkable capabilities in open-set retrieval. However, existing…
3D Scene ReconstructionNeRFObjectRetrieval+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+3Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentation
Pre-trained vision-language models, e.g., CLIP, have been successfully applied to zero-shot semantic segmentation. Existing CLIP-based approaches primarily utilize visual features from the last layer to align with text e…
SegmentationSemantic SegmentationZero-Shot Semantic SegmentationAlignZeg: Mitigating Objective Misalignment for Zero-shot Semantic Segmentation
A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accuracy of seen classes rather than unseen c…
Image SegmentationSegmentationSemantic SegmentationZero Shot Segmentation+1OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
The recent success of CLIP has demonstrated promising results in zero-shot semantic segmentation by transferring muiltimodal knowledge to pixel-level classification. However, leveraging pre-trained CLIP knowledge to clos…
Semantic SegmentationZero-Shot Semantic SegmentationSemantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products alongside onboard global positioning an…
SegmentationSemantic SegmentationZero-Shot Semantic Segmentation