Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation | Paper Citation Record

Observation f07a75a6-1f04-44df-9e77-4799db1fe2b4 · outbound

This paper cites Single-stage semantic segmentation from image labels.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Single-stage semantic segmentation from image labels

Reference 1

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Observation 0800f2fd-2408-4651-bb73-d2b737b94eeb · outbound

This paper cites Enhancing open-vocabulary semantic seg- mentation with prototype retrieval.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Enhancing open-vocabulary semantic seg- mentation with prototype retrieval

Reference 2

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Observation 493c422a-fecd-45b5-b9e4-7f12709acdbd · outbound

This paper cites Zerowaste dataset: To- wards deformable object segmentation in cluttered scenes.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Zerowaste dataset: To- wards deformable object segmentation in cluttered scenes

Reference 3

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Observation 55d548c3-5d63-4254-98e4-f9fe429a3555 · outbound

This paper cites What a mess: Multi-domain evaluation of zero-shot semantic segmentation.Advances in Neural Infor- mation Processing Systems, 36:73299–73311, 2023.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation What a mess: Multi-domain evaluation of zero-shot semantic segmentation.Advances in Neural Infor- mation Processing Systems, 36:73299–73311, 2023

Reference 4

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Observation 72edb67f-8192-4116-9b55-957b71213c55 · outbound

This paper cites Zero-shot semantic segmentation.NeurIPS, 32, 2019.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Zero-shot semantic segmentation.NeurIPS, 32, 2019

Reference 5

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Observation d100964b-1be7-44fb-95aa-ac3a87d74384 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Emerg- ing properties in self-supervised vision transformers

Reference 6

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Observation 3bc0245b-b6ea-43a1-9bb1-7984eba22b84 · outbound

This paper cites Modeling the background for incremental learning in semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Modeling the background for incremental learning in semantic segmentation

Reference 7

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Observation 0416b7bc-0cb0-430b-bd6d-b639ad0406ba · outbound

This paper cites Prototype-based Incremental Few-Shot Semantic Segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Prototype-based Incremental Few-Shot Semantic Segmentation

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:45:53.452406Z

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Observation fb2715f5-d1cd-4d8c-8b11-00790a14fe48 · outbound

This paper cites Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Learn- ing to generate text-grounded mask for open-world semantic segmentation from only image-text pairs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.445016Z

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Observation 0800f009-6dd0-425c-89a8-ee02a5a21550 · outbound

This paper cites Unleashing the potential of prompt engineering for large language models.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Unleashing the potential of prompt engineering for large language models

Reference 10

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Observation 629638ae-b1cb-4269-8908-33a766f4e94e · outbound

This paper cites Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Exploring open-vocabulary semantic segmentation from clip vision encoder distillation only

Reference 11

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Observation 3d058f83-16ab-44d5-a5f8-5c99730f166f · outbound

This paper cites MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.https : / / github.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark.https : / / github

Reference 12

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Observation 048fe4ce-5094-487e-8f18-171b37526b63 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 13

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Observation f195cb40-260f-48d6-8ecd-4cce337cb13a · outbound

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

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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Observation 77534581-3c24-4219-9bf5-20f05bbd47c1 · outbound

This paper cites A new large- scale food image segmentation dataset and its application to food calorie estimation based on grains of rice.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation A new large- scale food image segmentation dataset and its application to food calorie estimation based on grains of rice

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.390642Z

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Observation 68302ed3-8a0a-4b2d-b7f4-73a71b3a88db · outbound

This paper cites The pascal visual object classes (voc) challenge.International journal of computer vision, 88:303–338, 2010.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation The pascal visual object classes (voc) challenge.International journal of computer vision, 88:303–338, 2010

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.375963Z

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Observation 0bdab561-d681-45ce-a482-26108d0b1d83 · outbound

This paper cites Model- agnostic meta-learning for fast adaptation of deep networks.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Model- agnostic meta-learning for fast adaptation of deep networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.361612Z

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Observation 7f149794-cd90-4c0b-a5d0-7565706c9d3a · outbound

This paper cites Scal- ing open-vocabulary image segmentation with image-level labels.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Scal- ing open-vocabulary image segmentation with image-level labels

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.347185Z

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Observation 806dd133-c184-4104-8a42-42a1a2043644 · outbound

This paper cites Context-aware feature generation for zero- shot semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Context-aware feature generation for zero- shot semantic segmentation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.332169Z

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Observation 54afdd9b-f820-4eff-b87a-fc1d7939c084 · outbound

This paper cites Mvp-seg: Multi-view prompt learning for open-vocabulary semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Mvp-seg: Multi-view prompt learning for open-vocabulary semantic segmentation

Reference 20

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Observation a20e0fc1-0908-4a8a-912a-194482eb7b9a · outbound

This paper cites Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation

Reference 21

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Observation 4a620cf5-650a-4cd1-b63f-24ac029e4e96 · outbound

This paper cites Cost aggregation with 4d convolutional swin transformer for few-shot segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Cost aggregation with 4d convolutional swin transformer for few-shot segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.303219Z

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Observation 97c5f418-c224-4789-89d1-73d66cc6cc39 · outbound

This paper cites Open- clip.https : / / github.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Open- clip.https : / / github

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.288480Z

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Observation 182b3a74-4977-4807-a8ca-7d8249cdda60 · outbound

This paper cites Diffusion models for zero-shot open-vocabulary segmentation.arXiv e-prints, pages arXiv–2306, 2023.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Diffusion models for zero-shot open-vocabulary segmentation.arXiv e-prints, pages arXiv–2306, 2023

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.273110Z

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Observation 6da7114c-8038-46a0-8132-62a479f290d0 · outbound

This paper cites Segment any- thing.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Segment any- thing

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.257532Z

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Observation b5050f5b-42ac-4171-8050-fb0c1f3ce9b1 · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the national academy of sci- ences, 114(13):3521–3526, 2017.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the national academy of sci- ences, 114(13):3521–3526, 2017

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.242687Z

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Observation 3ba475b6-4270-4365-8c17-69f4971147bb · outbound

This paper cites Proxyclip: Proxy at- tention improves clip for open-vocabulary segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Proxyclip: Proxy at- tention improves clip for open-vocabulary segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.227682Z

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Observation 74c4d582-3c12-489a-a629-610dfde4e711 · outbound

This paper cites Adaptive prototype learning and allocation for few-shot segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Adaptive prototype learning and allocation for few-shot segmentation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.212236Z

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Observation 1c3b275e-e1d0-41ce-8206-877bfec78201 · outbound

This paper cites Multiple-Human Parsing in the Wild.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Multiple-Human Parsing in the Wild

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.035371Z

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Observation 8246476c-97ef-4575-8557-5904a5027230 · outbound

This paper cites Clip surgery for better explainability with enhancement in open- vocabulary tasks.arXiv e-prints, pages arXiv–2304, 2023.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Clip surgery for better explainability with enhancement in open- vocabulary tasks.arXiv e-prints, pages arXiv–2304, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.197681Z

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Observation 1d958cc3-dba2-4450-ba3b-52937d216dc8 · outbound

This paper cites Learning without forgetting.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Learning without forgetting

Reference 31

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Observation 666d3d19-53e1-4f1d-9461-42c7691ec837 · outbound

This paper cites Open-vocabulary semantic segmentation with mask-adapted clip.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Open-vocabulary semantic segmentation with mask-adapted clip

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.173005Z

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Observation 34844ea3-1bb0-4b8c-bb72-e1a089b2c5e1 · outbound

This paper cites Learning non-target knowledge for few- shot semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Learning non-target knowledge for few- shot semantic segmentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.159232Z

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Observation 9cb52e1d-d651-48db-9115-d528c14678ba · outbound

This paper cites Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching

Reference 34

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Observation d878ad29-2375-4ff2-bc77-621042c4779e · outbound

This paper cites A simple im- age segmentation framework via in-context examples.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation A simple im- age segmentation framework via in-context examples

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.145206Z

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Observation 549f8c6b-50ac-450a-86ef-7c4b9df0f0d3 · outbound

This paper cites Image segmentation using text and image prompts.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Image segmentation using text and image prompts

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.130169Z

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Observation 3624d1ab-5104-4877-80e4-3cb16da647f2 · outbound

This paper cites Uavid: A semantic segmentation dataset for uav imagery.ISPRS journal of photogrammetry and remote sensing, 165:108–119, 2020.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Uavid: A semantic segmentation dataset for uav imagery.ISPRS journal of photogrammetry and remote sensing, 165:108–119, 2020

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.114024Z

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Observation 5629eefe-2f0d-4a97-bdd8-6e7ce5e36b3a · outbound

This paper cites Hypercorre- lation squeeze for few-shot segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Hypercorre- lation squeeze for few-shot segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.098854Z

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Observation 97f7c763-32b4-4067-b8d6-bbed627cab2a · outbound

This paper cites mask dataset.https://universe.roboflow.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation mask dataset.https://universe.roboflow

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.084217Z

Source-reported events for the cited work

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Observation 71836c1e-64e7-40a1-8c27-979f1e442d8a · outbound

This paper cites Open vocabulary semantic segmentation with patch aligned con- trastive learning.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Open vocabulary semantic segmentation with patch aligned con- trastive learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.068952Z

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Observation aef46ded-96c6-4007-80a4-ffd3a2117d35 · outbound

This paper cites SAMIC: Segment Anything with In-Context Spatial Prompt Engineering.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation SAMIC: Segment Anything with In-Context Spatial Prompt Engineering

Reference 41

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Observation 97a5551d-9da9-46de-a047-f66b4b5e0532 · outbound

This paper cites Trash (v2).https : / / universe.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Trash (v2).https : / / universe

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.053907Z

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Observation a5f680bd-7471-4949-864a-94794f9a6c06 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation DINOv2: Learning Robust Visual Features without Supervision

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.096097Z

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Observation f1107969-8375-4a1a-bbcf-3e21fa1cfe07 · outbound

This paper cites Continual lifelong learning with neural networks: A review.Neural networks, 113:54–71,.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Continual lifelong learning with neural networks: A review.Neural networks, 113:54–71,

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.100519Z

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Observation 581c8984-c2d5-4cb8-9c73-fab07214db50 · outbound

This paper cites A closer look at self-training for zero-label semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation A closer look at self-training for zero-label semantic segmentation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.029285Z

Source-reported events for the cited work

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Observation 2b84f3b9-b925-47a0-af14-7b01c16f6502 · outbound

This paper cites Freeseg: Unified, universal and open-vocabulary im- age segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Freeseg: Unified, universal and open-vocabulary im- age segmentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:54.014182Z

Source-reported events for the cited work

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Observation 1a7da7fc-e7f6-4cfa-b9e0-c4e65e26cd9a · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Learn- ing transferable visual models from natural language super- vision

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.999161Z

Source-reported events for the cited work

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Observation 2aa6dad9-ba2e-49ee-9391-2011536e6254 · outbound

This paper cites Zero-shot text-to-image generation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Zero-shot text-to-image generation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.118432Z

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Observation 25d8415e-9ef7-4992-99da-d7ba09d72d31 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation SAM 2: Segment Anything in Images and Videos

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.122459Z

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Observation 1f94a279-c0f2-42c9-8653-8f42f68f4249 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation High-resolution image syn- thesis with latent diffusion models

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.126852Z

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Unavailable: canonical work link unavailable.

Observation 43ae1153-2c55-4443-af3c-452a70dce23b · outbound

This paper cites One-Shot Learning for Semantic Segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation One-Shot Learning for Semantic Segmentation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.131067Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a2cb2dd-46b9-47ea-ac97-0e713764bd90 · outbound

This paper cites Reco: Re- trieve and co-segment for zero-shot transfer.NeurIPS, 35: 33754–33767, 2022.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Reco: Re- trieve and co-segment for zero-shot transfer.NeurIPS, 35: 33754–33767, 2022

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.965974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e5c91157-c8e8-4a2b-9e53-b939df761d18 · outbound

This paper cites What does clip know about a red circle? visual prompt engineering for vlms.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation What does clip know about a red circle? visual prompt engineering for vlms

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.140307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d5d9d12-b5d7-4cc8-b8a6-9eb6bbfa359f · outbound

This paper cites Vrp-sam: Sam with visual reference prompt.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Vrp-sam: Sam with visual reference prompt

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.940864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 41dea74e-13af-4fc1-b791-cedf3288e918 · outbound

This paper cites abc dataset.https : / / universe.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation abc dataset.https : / / universe

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.925189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e4ee27d-78ec-4011-a228-56d31b5c171d · outbound

This paper cites Springer, 1998.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Springer, 1998

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.909431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ef639c4-2f0e-41a7-b06e-a5ee12f253ee · outbound

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

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Sclip: Rethinking self-attention for dense vision-language inference

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.157591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3abf3147-d918-49ab-ab98-2ce66c686ce1 · outbound

This paper cites Few-shot semantic seg- mentation with democratic attention networks.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Few-shot semantic seg- mentation with democratic attention networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.777981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6de3229a-ed07-4cfc-88c6-825745400d0c · outbound

This paper cites Sam-clip: Merging vision foundation models towards semantic and spatial understanding.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Sam-clip: Merging vision foundation models towards semantic and spatial understanding

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.763810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 04a3d8c1-92aa-445f-b542-436a4bb97f5b · outbound

This paper cites Loveda: A remote sensing land-cover dataset for domain adaptive semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Loveda: A remote sensing land-cover dataset for domain adaptive semantic segmentation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.170470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 122cd54f-83a1-44b2-a788-15d5379217e8 · outbound

This paper cites Review of large vision models and visual prompt engineering.Meta-Radiology, 1(3):100047,.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Review of large vision models and visual prompt engineering.Meta-Radiology, 1(3):100047,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.739252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e3f1bff4-2442-4be1-95bc-0856142408e7 · outbound

This paper cites Panet: Few-shot image semantic segmenta- tion with prototype alignment.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Panet: Few-shot image semantic segmenta- tion with prototype alignment

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.724911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9cd560e3-ad50-4481-9c31-1caef4044439 · outbound

This paper cites Images speak in images: A generalist painter for in-context visual learning.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Images speak in images: A generalist painter for in-context visual learning

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.710265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a9e7135b-cfba-483a-93d5-f771af26e96e · outbound

This paper cites Seggpt: Towards seg- menting everything in context.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Seggpt: Towards seg- menting everything in context

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.695800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5cd2d9b5-81c7-4a44-b581-b7bd0d30f92f · outbound

This paper cites Clip-dinoiser: Teaching clip a few dino tricks for open- vocabulary semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Clip-dinoiser: Teaching clip a few dino tricks for open- vocabulary semantic segmentation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.680376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 21c1811d-41d2-40a0-a02e-b706920559a8 · outbound

This paper cites Semantic projection network for zero-and few-label semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Semantic projection network for zero-and few-label semantic segmentation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.665565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b94b039f-64cb-491c-982f-749e0e9479b4 · outbound

This paper cites Cat-sam: Con- ditional tuning for few-shot adaptation of segment anything model.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Cat-sam: Con- ditional tuning for few-shot adaptation of segment anything model

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.650932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 85b6580a-bb42-4cfc-a07d-792b869b0023 · outbound

This paper cites piiz dataset.https://universe.roboflow.com/ y-rgb4q/piiz, 2023.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation piiz dataset.https://universe.roboflow.com/ y-rgb4q/piiz, 2023

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.635361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3bbe566d-2640-4382-a278-9c798632d8cd · outbound

This paper cites Contin- ual learning through synaptic intelligence.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Contin- ual learning through synaptic intelligence

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.621040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f00ecdaf-63f4-4163-87ea-4fa2721f3a2d · outbound

This paper cites Bridge the points: Graph-based few-shot segment anything semantically.NeurIPS, 37:33232–33261, 2024.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Bridge the points: Graph-based few-shot segment anything semantically.NeurIPS, 37:33232–33261, 2024

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.606550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c69317a7-63c7-4d72-8844-babfe4fd5bac · outbound

This paper cites Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.591720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0fd5bbf9-647c-4228-b5b0-909e31b42e8e · outbound

This paper cites Few-shot segmentation via cycle-consistent trans- former.NeurIPS, 34:21984–21996, 2021.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Few-shot segmentation via cycle-consistent trans- former.NeurIPS, 34:21984–21996, 2021

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.577198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6ba3fe44-ec12-4c7d-b864-e396104136a0 · outbound

This paper cites Pidray: A large-scale x-ray benchmark for real-world prohibited item detection.International Journal of Computer Vision, 131 (12):3170–3192, 2023.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Pidray: A large-scale x-ray benchmark for real-world prohibited item detection.International Journal of Computer Vision, 131 (12):3170–3192, 2023

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.561329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f6287afd-ec0b-4319-b559-2fc287983d07 · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Personalize Segment Anything Model with One Shot

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T23:45:53.231801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad0e7724-d552-4e0e-9caa-c93ddb730b97 · outbound

This paper cites Scene parsing through ade20k dataset.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Scene parsing through ade20k dataset

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.545837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a1daea86-e065-451e-8409-0fa59483d560 · outbound

This paper cites Extract free dense labels from clip.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Extract free dense labels from clip

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.529975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 739e725d-aec8-40ad-95f5-45c03c978df1 · outbound

This paper cites Generalized decoding for pixel, image, and language.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Generalized decoding for pixel, image, and language

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.514793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fda02608-a3cd-4ebc-a3f1-1dd456f00b19 · outbound

This paper cites an unresolved cited work.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:45:53.499588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fe53199c-e760-42ae-a120-a90201a346ce · outbound

This paper cites Open-vocabulary methods.To ensure a fair comparison, we report the results for open-vocabulary methods without applying any mask refinement step (e.g.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Open-vocabulary methods.To ensure a fair comparison, we report the results for open-vocabulary methods without applying any mask refinement step (e.g

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.483961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6cce04f5-8251-4902-85b4-7fff1216dc52 · outbound

This paper cites ADE20KThe ADE20K [75] dataset is made up of 150 classes.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation ADE20KThe ADE20K [75] dataset is made up of 150 classes

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:45:53.468004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers