A Survey on Training-free Open-Vocabulary Semantic Segmentation | Paper Citation Record

Observation 162dfefc-54f6-43fc-8a0d-4e56258b2c13 · outbound

This paper cites Single-Stage Semantic Segmentation from Image Labels.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Single-Stage Semantic Segmentation from Image Labels

Reference 1

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This paper cites Arda Aydın, Efe Mert C ¸ ırpar, Elvin Abdinli, Gozde Unal, and Yusuf H.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Arda Aydın, Efe Mert C ¸ ırpar, Elvin Abdinli, Gozde Unal, and Yusuf H

Reference 2

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This paper cites Self-calibrated clip for training-free open-vocabulary segmentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Self-calibrated clip for training-free open-vocabulary segmentation, 2024

Reference 3

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This paper cites Fossil: Free open-vocabulary semantic seg- mentation through synthetic references retrieval.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Fossil: Free open-vocabulary semantic seg- mentation through synthetic references retrieval

Reference 4

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This paper cites Training-free open- vocabulary segmentation with offline diffusion-augmented prototype generation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Training-free open- vocabulary segmentation with offline diffusion-augmented prototype generation, 2024

Reference 5

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This paper cites Grounding everything: Emerging localiza- tion properties in vision-language transformers, 2023.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Grounding everything: Emerging localiza- tion properties in vision-language transformers, 2023

Reference 6

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Observation dfcefeb0-5b38-4b87-a2a6-0e8025aaca35 · outbound

This paper cites Breunig, Hans-Peter Kriegel, Raymond T.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Breunig, Hans-Peter Kriegel, Raymond T

Reference 7

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This paper cites Zero-Shot Semantic Segmentation.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Zero-Shot Semantic Segmentation

Reference 8

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Observation c664261f-004f-46bd-851b-a85e53265907 · outbound

This paper cites COCO-Stuff: Thing and Stuff Classes in Context.

A Survey on Training-free Open-Vocabulary Semantic Segmentation COCO-Stuff: Thing and Stuff Classes in Context

Reference 9

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This paper cites nuScenes: A multimodal dataset for autonomous driving.

A Survey on Training-free Open-Vocabulary Semantic Segmentation nuScenes: A multimodal dataset for autonomous driving

Reference 10

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This paper cites A computational approach to edge detection.

A Survey on Training-free Open-Vocabulary Semantic Segmentation A computational approach to edge detection

Reference 11

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This paper cites Emerging Properties in Self-Supervised Vision Transformers.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Emerging Properties in Self-Supervised Vision Transformers

Reference 12

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A Survey on Training-free Open-Vocabulary Semantic Segmentation Unresolved cited work

Reference 13

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Observation 2d86ad36-7574-4981-9afd-3ff5e7d12756 · outbound

This paper cites Masked-attention Mask Transformer for Universal Image Segmentation.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Masked-attention Mask Transformer for Universal Image Segmentation

Reference 14

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Observation 3a0e4d14-680c-42b5-bb33-d7e04e1ca2f9 · outbound

This paper cites Per-Pixel Classification is Not All You Need for Semantic Segmentation.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Per-Pixel Classification is Not All You Need for Semantic Segmentation

Reference 15

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This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding.

A Survey on Training-free Open-Vocabulary Semantic Segmentation The Cityscapes Dataset for Semantic Urban Scene Understanding

Reference 16

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This paper cites Freeseg-diff: Training-free open-vocabulary segmentation with diffusion models, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Freeseg-diff: Training-free open-vocabulary segmentation with diffusion models, 2024

Reference 17

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Observation 3f82ee31-c044-449f-980c-1b08b57f684c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

A Survey on Training-free Open-Vocabulary Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

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This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise.

A Survey on Training-free Open-Vocabulary Semantic Segmentation A density-based algorithm for discovering clusters in large spatial databases with noise

Reference 19

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This paper cites The pascal visual object classes (voc) challenge.

A Survey on Training-free Open-Vocabulary Semantic Segmentation The pascal visual object classes (voc) challenge

Reference 20

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This paper cites Pay attention to your neighbours: Training-free open-vocabulary semantic segmentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Pay attention to your neighbours: Training-free open-vocabulary semantic segmentation, 2024

Reference 21

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This paper cites Mask R-CNN.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Mask R-CNN

Reference 22

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This paper cites Masked Autoencoders Are Scalable Vision Learners.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Masked Autoencoders Are Scalable Vision Learners

Reference 23

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This paper cites Zemel, and M.A.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Zemel, and M.A

Reference 24

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Observation 6fe08cd4-814e-4ed4-a22a-c6c13b88c374 · outbound

This paper cites Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling

Reference 25

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This paper cites Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

Reference 26

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Observation 9b5fd46d-9ee2-4d40-b3e3-180275e3eb72 · outbound

This paper cites In defense of lazy vi- sual grounding for open-vocabulary semantic segmentation,.

A Survey on Training-free Open-Vocabulary Semantic Segmentation In defense of lazy vi- sual grounding for open-vocabulary semantic segmentation,

Reference 27

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Observation f44b9b9c-e28f-49a5-94af-2a154a31bc43 · outbound

This paper cites Diffusion models for open-vocabulary segmen- tation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Diffusion models for open-vocabulary segmen- tation, 2024

Reference 28

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This paper cites Maskdiffusion: Exploiting pre-trained diffusion models for semantic seg- mentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Maskdiffusion: Exploiting pre-trained diffusion models for semantic seg- mentation, 2024

Reference 29

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This paper cites Tag: Guidance-free open-vocabulary semantic segmentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Tag: Guidance-free open-vocabulary semantic segmentation, 2024

Reference 30

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This paper cites Distilling spectral graph for object-context aware open-vocabulary semantic segmenta- tion, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Distilling spectral graph for object-context aware open-vocabulary semantic segmenta- tion, 2024

Reference 31

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This paper cites Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick

Reference 32

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This paper cites Clearclip: Decom- posing clip representations for dense vision-language infer- ence, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Clearclip: Decom- posing clip representations for dense vision-language infer- ence, 2024

Reference 33

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This paper cites Proxyclip: Proxy attention improves clip for open-vocabulary segmentation,.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Proxyclip: Proxy attention improves clip for open-vocabulary segmentation,

Reference 34

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This paper cites A closer look at the explainability of contrastive language-image pre-training, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation A closer look at the explainability of contrastive language-image pre-training, 2024

Reference 35

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This paper cites A ConvNet for the 2020s.

A Survey on Training-free Open-Vocabulary Semantic Segmentation A ConvNet for the 2020s

Reference 36

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A Survey on Training-free Open-Vocabulary Semantic Segmentation Fully Convolutional Networks for Semantic Segmentation

Reference 37

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This paper cites Segment anything in medical images.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Segment anything in medical images

Reference 38

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Observation 37c7a82c-43d1-4be6-b5e7-09b26a6ebd90 · outbound

This paper cites Meyer and S.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Meyer and S

Reference 39

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Observation b6aaa17f-2195-46d6-98f7-9b8d07f08f6e · outbound

This paper cites The role of context for object detection and se- mantic segmentation in the wild.

A Survey on Training-free Open-Vocabulary Semantic Segmentation The role of context for object detection and se- mantic segmentation in the wild

Reference 40

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This paper cites Emerdiff: Emerging pixel-level semantic knowledge in diffusion models, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Emerdiff: Emerging pixel-level semantic knowledge in diffusion models, 2024

Reference 41

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This paper cites Dinov2: Learning robust visual features with- out supervision, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Dinov2: Learning robust visual features with- out supervision, 2024

Reference 42

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Observation 273a4529-29ee-4d56-bcd7-6b39a4a71808 · outbound

This paper cites A threshold selection method from gray- level histograms.

A Survey on Training-free Open-Vocabulary Semantic Segmentation A threshold selection method from gray- level histograms

Reference 43

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Observation cf151e72-8693-4a6f-9b6e-b6efe336bed7 · outbound

This paper cites Cattle segmentation and contour extraction based on mask r-cnn for precision livestock farming.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Cattle segmentation and contour extraction based on mask r-cnn for precision livestock farming

Reference 44

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Observation 9bbb184b-0b76-46ee-b46d-7b42e3bde8c4 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Learning Transferable Visual Models From Natural Language Supervision

Reference 45

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This paper cites High-Resolution Image Synthesis with Latent Diffusion Models.

A Survey on Training-free Open-Vocabulary Semantic Segmentation High-Resolution Image Synthesis with Latent Diffusion Models

Reference 47

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Observation e214adb3-75dc-4c3c-b003-9daf81fa5411 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

A Survey on Training-free Open-Vocabulary Semantic Segmentation U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 48

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Observation ed19a431-bf15-4dd7-a5e0-b0eff50a3591 · outbound

This paper cites Deep learning based real-time industrial framework for rotten and fresh fruit detection using semantic segmentation.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Deep learning based real-time industrial framework for rotten and fresh fruit detection using semantic segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:57.942026Z

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Observation ffb49e98-6bf8-4660-8338-645d3247122e · outbound

This paper cites Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

Reference 50

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Observation 9975889e-a410-443d-bb4e-0c7eba94feda · outbound

This paper cites Ex- plore the potential of clip for training-free open vocabulary semantic segmentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Ex- plore the potential of clip for training-free open vocabulary semantic segmentation, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:57.810811Z

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Observation 73e56832-5e65-4569-b2d4-c573d23b2a12 · outbound

This paper cites an unresolved cited work.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Unresolved cited work

Reference 52

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Observation 2074cfe0-15bc-4b82-ab62-1b7ac6d1ecad · outbound

This paper cites Harnessing vi- sion foundation models for high-performance, training-free open vocabulary segmentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Harnessing vi- sion foundation models for high-performance, training-free open vocabulary segmentation, 2024

Reference 53

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This paper cites Reco: Re- trieve and co-segment for zero-shot transfer, 2022.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Reco: Re- trieve and co-segment for zero-shot transfer, 2022

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:57.411128Z

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This paper cites Unsupervised object local- ization: Observing the background to discover objects, 2023.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Unsupervised object local- ization: Observing the background to discover objects, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:57.294462Z

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This paper cites Cliper: Hierarchically improving spatial represen- tation of clip for open-vocabulary semantic segmentation,.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Cliper: Hierarchically improving spatial represen- tation of clip for open-vocabulary semantic segmentation,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:56.983603Z

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This paper cites Clip as rnn: Segment countless visual concepts without training endeavor, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Clip as rnn: Segment countless visual concepts without training endeavor, 2024

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:56.723736Z

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This paper cites Lema, Oscar D.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Lema, Oscar D

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:56.506492Z

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Observation 7863f2b6-01dd-4d8d-87c8-093f43aab2d1 · outbound

This paper cites Attention Is All You Need.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Attention Is All You Need

Reference 59

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This paper cites Sclip: Rethinking self-attention for dense vision-language inference, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Sclip: Rethinking self-attention for dense vision-language inference, 2024

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:56.364925Z

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Observation d0b4491c-d40b-48f5-8b84-88791906f9a2 · outbound

This paper cites Diffusion model is secretly a training-free open vocabulary semantic segmenter,.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Diffusion model is secretly a training-free open vocabulary semantic segmenter,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:56.219556Z

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Observation 93d8a46d-c2bf-4f70-8316-db4018842321 · outbound

This paper cites Tokencut: Segmenting objects in images and videos with self-supervised transformer and normalized cut, 2023.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Tokencut: Segmenting objects in images and videos with self-supervised transformer and normalized cut, 2023

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:56.106036Z

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This paper cites Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation,.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Image-to-image matching via foundation models: A new perspective for open-vocabulary semantic segmentation,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.961957Z

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Observation 69eeb3a7-b54f-4833-a642-7fae32d68c7b · outbound

This paper cites Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free,.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Clip-diy: Clip dense infer- ence yields open-vocabulary semantic segmentation for-free,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.792238Z

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Observation f66bdd74-fe76-4c02-af23-bd92753f26cb · outbound

This paper cites Groupvit: Semantic segmentation emerges from text supervision, 2022.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Groupvit: Semantic segmentation emerges from text supervision, 2022

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.630832Z

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Observation c9b21baa-48c3-46bb-ab4c-f3c22877fd8d · outbound

This paper cites Tuning-free universally- supervised semantic segmentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Tuning-free universally- supervised semantic segmentation, 2024

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.510378Z

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This paper cites Tuning-free universally- supervised semantic segmentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Tuning-free universally- supervised semantic segmentation, 2024

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.331364Z

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Observation 335789c7-b1b0-4861-9761-720d9d8f3e87 · outbound

This paper cites Resclip: Residual attention for training-free dense vision- language inference, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Resclip: Residual attention for training-free dense vision- language inference, 2024

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.215557Z

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Observation fed52601-e2c2-46c0-b7c1-c4e6bc624e65 · outbound

This paper cites Advanced agricultural disease image recognition technologies: A re- view.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Advanced agricultural disease image recognition technologies: A re- view

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:55.018589Z

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Observation d9763a44-a80d-40cd-bafc-a4e5895e60d4 · outbound

This paper cites Sigmoid loss for language image pre-training,.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Sigmoid loss for language image pre-training,

Reference 70

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Observation 101100c7-b134-47c4-a6cb-4889f9d2aab2 · outbound

This paper cites Corrclip: Recon- structing correlations in clip with off-the-shelf foundation models for open-vocabulary semantic segmentation, 2024.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Corrclip: Recon- structing correlations in clip with off-the-shelf foundation models for open-vocabulary semantic segmentation, 2024

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:54.906021Z

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Observation e0bcf50c-1c35-4713-a608-c94c167d1241 · outbound

This paper cites Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Reference 72

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Observation a811e6a5-08a8-40b9-a646-6dfbaf661822 · outbound

This paper cites Semantic Understanding of Scenes through the ADE20K Dataset.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Semantic Understanding of Scenes through the ADE20K Dataset

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T13:15:40.886189Z

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Observation 52d97c00-de2e-478c-9625-3448408ba26f · outbound

This paper cites Extract free dense labels from clip, 2022.

A Survey on Training-free Open-Vocabulary Semantic Segmentation Extract free dense labels from clip, 2022

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:15:54.714808Z

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