Data to 7 Oct 2026

Context-aware Feature Generation for Zero-shot Semantic Segmentation

arXiv 2008.06893 ↗First posted 16 Aug 2020cs.CVcited by 94

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Abstract (arXiv)

Existing semantic segmentation models heavily rely on dense pixel-wise annotations. To reduce the annotation pressure, we focus on a challenging task named zero-shot semantic segmentation, which aims to segment unseen objects with zero annotations. This task can be accomplished by transferring knowledge across categories via semantic word embeddings. In this paper, we propose a novel context-aware feature generation method for zero-shot segmentation named CaGNet. In particular, with the observation that a pixel-wise feature highly depends on its contextual information, we insert a contextual module in a segmentation network to capture the pixel-wise contextual information, which guides the process of generating more diverse and context-aware features from semantic word embeddings. Our method achieves state-of-the-art results on three benchmark datasets for zero-shot segmentation. Codes are available at: https://github.com/bcmi/CaGNet-Zero-Shot-Semantic-Segmentation.

Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Aug 2020.

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Citing papers per year: 2021: 14; 2022: 13; 2023: 43; 2024: 10; 2025: 13; 2026: 1.

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2022Language-driven Semantic Segmentationcited by 711
2021Extract Free Dense Labels from CLIPcited by 580
2021Image Segmentation Using Text and Image Promptscited by 509
2023Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fieldscited by 361
2021A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Modelcited by 223
2021Decoupling Zero-Shot Semantic Segmentationcited by 198
2022ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentationcited by 178
2022PLA: Language-Driven Open-Vocabulary 3D Scene Understandingcited by 176
2023Towards Open Vocabulary Learning: A Surveycited by 165
2023DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Modelscited by 162
2023DatasetDM: Synthesizing Data with Perception Annotations Using Diffusion Modelscited by 122
2022ReCo: Retrieve and Co-segment for Zero-shot Transfercited by 115

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2023Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervisioncited by 99
2023FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentationcited by 94
2023CLIP-DINOiser: Teaching CLIP a few DINO tricks for open-vocabulary semantic segmentationcited by 94
2022Perceptual Grouping in Contrastive Vision-Language Modelscited by 82
2023Open-vocabulary Object Segmentation with Diffusion Modelscited by 82
2022Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embeddingcited by 67
2023Diffusion Models for Open-Vocabulary Segmentationcited by 63
2023SATR: Zero-Shot Semantic Segmentation of 3D Shapescited by 62
2023A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Futurecited by 59
2021A Closer Look at Self-training for Zero-Label Semantic Segmentationcited by 57
2023Learning Mask-aware CLIP Representations for Zero-Shot Segmentationcited by 57
2021Generative Zero-Shot Learning for Semantic Segmentation of 3D Point Cloudscited by 55
2021Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentationcited by 53
2022A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Predictioncited by 46
2021SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentationcited by 41
2023Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Networkcited by 40
2022Open-vocabulary Semantic Segmentation with Frozen Vision-Language Modelscited by 39
2023CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Freecited by 39
2023Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understandingcited by 38
2024In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentationcited by 38
2023Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognitioncited by 35
2023DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Modelcited by 30
2023AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregationcited by 29
2023Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentationcited by 28
2023MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentationcited by 25
2023Multi-Modal Prototypes for Open-World Semantic Segmentationcited by 22
2024MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesiscited by 22
2023See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Datacited by 21
2024Cascade-CLIP: Cascaded Vision-Language Embeddings Alignment for Zero-Shot Semantic Segmentationcited by 17
2022Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overviewcited by 16
2023ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Promptscited by 16
2023Delving into Shape-aware Zero-shot Semantic Segmentationcited by 16
2023Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentationcited by 16
2023IFSeg: Image-free Semantic Segmentation via Vision-Language Modelcited by 15
2023Towards Training-free Open-world Segmentation via Image Prompt Foundation Modelscited by 15
2023Emergent Open-Vocabulary Semantic Segmentation from Off-the-shelf Vision-Language Modelscited by 15
2023ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Promptingcited by 15
2023TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentationcited by 14

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