Zero-Shot Semantic Segmentation
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Benchmarks
Most implemented
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
FLAIR: VLM with Fine-grained Language-informed Image Representations
A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
Context-aware Feature Generation for Zero-shot Semantic Segmentation
Zero-Shot Semantic Segmentation
Papers
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 Understanding