ConText: Driving In-context Learning for Text Removal and Segmentation | Paper Citation Record

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This paper cites What learning algorithm is in-context learning? Investigations with linear models.

ConText: Driving In-context Learning for Text Removal and Segmentation What learning algorithm is in-context learning? Investigations with linear models

Reference 1

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Observation eefa5975-0c1d-45d7-b7aa-1ac9c1122abd · outbound

This paper cites L., Darrell, T., Malik, J., and Efros, A.

ConText: Driving In-context Learning for Text Removal and Segmentation L., Darrell, T., Malik, J., and Efros, A

Reference 2

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ConText: Driving In-context Learning for Text Removal and Segmentation Visual prompting via image inpainting

Reference 3

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This paper cites Scene text removal via cascaded text stroke detection and erasing.

ConText: Driving In-context Learning for Text Removal and Segmentation Scene text removal via cascaded text stroke detection and erasing

Reference 4

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This paper cites Coco\_ts dataset: pixel--level annotations based on weak supervision for scene text segmentation.

ConText: Driving In-context Learning for Text Removal and Segmentation Coco\_ts dataset: pixel--level annotations based on weak supervision for scene text segmentation

Reference 5

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This paper cites D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.

ConText: Driving In-context Learning for Text Removal and Segmentation D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

Reference 6

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This paper cites Textdiffuser: Diffusion models as text painters.

ConText: Driving In-context Learning for Text Removal and Segmentation Textdiffuser: Diffusion models as text painters

Reference 7

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This paper cites Encoder-decoder with atrous separable convolution for semantic image segmentation.

ConText: Driving In-context Learning for Text Removal and Segmentation Encoder-decoder with atrous separable convolution for semantic image segmentation

Reference 8

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ConText: Driving In-context Learning for Text Removal and Segmentation Unresolved cited work

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This paper cites and Chen, P.-I.

ConText: Driving In-context Learning for Text Removal and Segmentation and Chen, P.-I

Reference 10

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This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

ConText: Driving In-context Learning for Text Removal and Segmentation Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 11

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This paper cites A Survey on In-context Learning.

ConText: Driving In-context Learning for Text Removal and Segmentation A Survey on In-context Learning

Reference 12

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

ConText: Driving In-context Learning for Text Removal and Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 13

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This paper cites Modeling stroke mask for end-to-end text erasing.

ConText: Driving In-context Learning for Text Removal and Segmentation Modeling stroke mask for end-to-end text erasing

Reference 14

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This paper cites Progressive scene text erasing with self-supervision.

ConText: Driving In-context Learning for Text Removal and Segmentation Progressive scene text erasing with self-supervision

Reference 15

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Observation f57efffc-cc56-4f0a-a6ef-e02a7a999e98 · outbound

This paper cites Progressive scene text erasing with self-supervision.

ConText: Driving In-context Learning for Text Removal and Segmentation Progressive scene text erasing with self-supervision

Reference 16

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ConText: Driving In-context Learning for Text Removal and Segmentation M., Loy, C

Reference 17

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ConText: Driving In-context Learning for Text Removal and Segmentation S., and Valiant, G

Reference 18

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This paper cites Masked autoencoders are scalable vision learners.

ConText: Driving In-context Learning for Text Removal and Segmentation Masked autoencoders are scalable vision learners

Reference 19

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Observation 9cd4d61a-20de-4e3e-9175-e1100c254dac · outbound

This paper cites J., and Wang, Z.

ConText: Driving In-context Learning for Text Removal and Segmentation J., and Wang, Z

Reference 20

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Observation 73a8556a-1506-429d-ae0a-1a7f50cf9f74 · outbound

This paper cites Self-supervised text erasing with controllable image synthesis.

ConText: Driving In-context Learning for Text Removal and Segmentation Self-supervised text erasing with controllable image synthesis

Reference 21

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Observation ee96fb5c-51a6-4d56-bd55-32d824867d2f · outbound

This paper cites C., and Gevers, T.

ConText: Driving In-context Learning for Text Removal and Segmentation C., and Gevers, T

Reference 22

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ConText: Driving In-context Learning for Text Removal and Segmentation G., Mestre, S

Reference 23

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Observation 56747e1e-645a-4343-8212-9655b6f646d6 · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation Unresolved cited work

Reference 24

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Observation 893be5e1-032c-4e13-865c-953e35a68c45 · outbound

This paper cites Segment Anything.

ConText: Driving In-context Learning for Text Removal and Segmentation Segment Anything

Reference 25

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Observation 34f3e907-1c2c-4ebc-b09b-74e9697d2c07 · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation Crafting papers on machine learning

Reference 26

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ConText: Driving In-context Learning for Text Removal and Segmentation and Choi, C

Reference 27

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This paper cites Monte carlo linear clustering with single-point supervision is enough for infrared small target detection.

ConText: Driving In-context Learning for Text Removal and Segmentation Monte carlo linear clustering with single-point supervision is enough for infrared small target detection

Reference 28

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Observation d11afdba-80b7-450e-87f2-7f6366d0e1dc · outbound

This paper cites Ddaug: Differentiable data augmentation for weakly supervised semantic segmentation.

ConText: Driving In-context Learning for Text Removal and Segmentation Ddaug: Differentiable data augmentation for weakly supervised semantic segmentation

Reference 29

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Observation 0c1eef5f-07d5-48cb-95aa-adf43fed8f2b · outbound

This paper cites The Closeness of In-Context Learning and Weight Shifting for Softmax Regression.

ConText: Driving In-context Learning for Text Removal and Segmentation The Closeness of In-Context Learning and Weight Shifting for Softmax Regression

Reference 30

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ConText: Driving In-context Learning for Text Removal and Segmentation and Qiu, X

Reference 31

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Observation 00aed386-7f3a-4578-8ca5-bcd4af8eaeab · outbound

This paper cites Erasenet: End-to-end text removal in the wild.

ConText: Driving In-context Learning for Text Removal and Segmentation Erasenet: End-to-end text removal in the wild

Reference 32

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Observation becf9f31-f948-4a4b-a7db-a51bfe537e19 · outbound

This paper cites Don’t forget me: accurate background recovery for text removal via modeling local-global context.

ConText: Driving In-context Learning for Text Removal and Segmentation Don’t forget me: accurate background recovery for text removal via modeling local-global context

Reference 33

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Observation e9540eb1-c80a-4d77-9659-1f7a2f94155a · outbound

This paper cites Don’t forget me: accurate background recovery for text removal via modeling local-global context.

ConText: Driving In-context Learning for Text Removal and Segmentation Don’t forget me: accurate background recovery for text removal via modeling local-global context

Reference 34

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ConText: Driving In-context Learning for Text Removal and Segmentation Audio-visual segmentation via unlabeled frame exploitation

Reference 35

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Observation c8c7bd07-5992-4538-bec5-e34a6126e830 · outbound

This paper cites In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering.

ConText: Driving In-context Learning for Text Removal and Segmentation In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering

Reference 36

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This paper cites Wdnet: Watermark-decomposition network for visible watermark removal.

ConText: Driving In-context Learning for Text Removal and Segmentation Wdnet: Watermark-decomposition network for visible watermark removal

Reference 37

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Observation 2362b71d-5e5e-4265-b6a1-806153e50149 · outbound

This paper cites Towards end-to-end unified scene text detection and layout analysis.

ConText: Driving In-context Learning for Text Removal and Segmentation Towards end-to-end unified scene text detection and layout analysis

Reference 38

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ConText: Driving In-context Learning for Text Removal and Segmentation and Zhu, A

Reference 39

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Observation 7e1baaa5-c0cb-4815-8572-b6fda6997e4e · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation Unresolved cited work

Reference 40

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Observation f0c25978-acc9-480c-a66a-5a0f132e5c69 · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery

Reference 41

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This paper cites Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection.

ConText: Driving In-context Learning for Text Removal and Segmentation Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection

Reference 42

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ConText: Driving In-context Learning for Text Removal and Segmentation Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 43

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ConText: Driving In-context Learning for Text Removal and Segmentation Conditional Generative Adversarial Nets

Reference 44

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ConText: Driving In-context Learning for Text Removal and Segmentation Scene text eraser

Reference 45

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ConText: Driving In-context Learning for Text Removal and Segmentation Fine-grained visible watermark removal

Reference 46

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Observation c149eaec-477e-4c87-b999-9b5a6e5b1018 · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation Unresolved cited work

Reference 47

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Observation decb7db4-470f-420c-97f5-29e108ef6049 · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation What in-context learning “learns” in-context: Disentangling task recognition and task learning

Reference 48

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Observation 9bf0247d-7892-4a4b-91b8-512030d5a3f5 · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation Viteraser: Harnessing the power of vision transformers for scene text removal with segmim pretraining

Reference 49

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Observation 81457810-b5e9-404d-98a6-9b25fe1a3bff · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation Upocr: Towards unified pixel-level ocr interface

Reference 50

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ConText: Driving In-context Learning for Text Removal and Segmentation Image-to-image translation with conditional adversarial networks

Reference 51

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ConText: Driving In-context Learning for Text Removal and Segmentation Looking from a higher-level perspective: Attention and recognition enhanced multi-scale scene text segmentation

Reference 52

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ConText: Driving In-context Learning for Text Removal and Segmentation High-resolution image synthesis with latent diffusion models

Reference 53

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Observation d76af8e4-aafd-4869-9b31-541310841d09 · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation Learning To Retrieve Prompts for In-Context Learning

Reference 54

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ConText: Driving In-context Learning for Text Removal and Segmentation and Coustaty, M

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.146919Z

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Observation 28371d84-9f29-4461-852d-8375f006c0d9 · outbound

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ConText: Driving In-context Learning for Text Removal and Segmentation What does CLIP know about a red circle? Visual prompt engineering for VLMs

Reference 56

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Observation b4f268b6-1591-41d8-b67b-8a6919d4a76f · outbound

This paper cites Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition.

ConText: Driving In-context Learning for Text Removal and Segmentation Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition

Reference 57

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2c2a308e-755a-48a6-b842-81e0f867e04e · outbound

This paper cites Selective Annotation Makes Language Models Better Few-Shot Learners.

ConText: Driving In-context Learning for Text Removal and Segmentation Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:11.803292Z

Source-reported events for the cited work

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Observation 8c28182a-6523-4648-81d8-81dd1a7ab6c0 · outbound

This paper cites Exploring effective factors for improving visual in-context learning.

ConText: Driving In-context Learning for Text Removal and Segmentation Exploring effective factors for improving visual in-context learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:11.880006Z

Source-reported events for the cited work

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Observation e73ac5fc-b29b-474e-bb8b-76910379fc4d · outbound

This paper cites Stroke-based scene text erasing using synthetic data for training.

ConText: Driving In-context Learning for Text Removal and Segmentation Stroke-based scene text erasing using synthetic data for training

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.132097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e330387-f44d-4a03-8199-8497f1084f88 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

ConText: Driving In-context Learning for Text Removal and Segmentation LLaMA: Open and Efficient Foundation Language Models

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:12.057003Z

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Observation 4b7c4cc1-a110-4736-b776-24b0427403d6 · outbound

This paper cites Mtrnet++: One-stage mask-based scene text eraser.

ConText: Driving In-context Learning for Text Removal and Segmentation Mtrnet++: One-stage mask-based scene text eraser

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.116920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d29505e-c3b9-4d3b-a09d-f438a7fc3428 · outbound

This paper cites N., Kim, S.

ConText: Driving In-context Learning for Text Removal and Segmentation N., Kim, S

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.102695Z

Source-reported events for the cited work

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Observation 3f36432b-c7b4-4344-ae3a-a3d14784ed45 · outbound

This paper cites Transformers learn in-context by gradient descent.

ConText: Driving In-context Learning for Text Removal and Segmentation Transformers learn in-context by gradient descent

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:12.307578Z

Source-reported events for the cited work

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Observation 3990a1fb-f6fe-4c81-a60f-da53eadcb03f · outbound

This paper cites Deep high-resolution representation learning for visual recognition.

ConText: Driving In-context Learning for Text Removal and Segmentation Deep high-resolution representation learning for visual recognition

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.077494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7c086cfc-74b3-45d2-9d1b-60353a67b8ae · outbound

This paper cites Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning.

ConText: Driving In-context Learning for Text Removal and Segmentation Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:12.487239Z

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Observation 2364f9f8-3fe6-4956-8ab4-8931e7f692b1 · outbound

This paper cites Chain-of-Thought Reasoning Without Prompting.

ConText: Driving In-context Learning for Text Removal and Segmentation Chain-of-Thought Reasoning Without Prompting

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:12.585825Z

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Observation f7862466-fa67-4f3c-9a34-c4c2afb0ad7e · outbound

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

ConText: Driving In-context Learning for Text Removal and Segmentation Images speak in images: A generalist painter for in-context visual learning

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.061394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87e79fcf-7812-4f42-ac2f-69c277c68de9 · outbound

This paper cites Textformer: component-aware text segmentation with transformer.

ConText: Driving In-context Learning for Text Removal and Segmentation Textformer: component-aware text segmentation with transformer

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.046564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1038fe8b-c7cd-44f3-8436-a5b918634933 · outbound

This paper cites SegGPT: Segmenting Everything In Context.

ConText: Driving In-context Learning for Text Removal and Segmentation SegGPT: Segmenting Everything In Context

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:12.851868Z

Source-reported events for the cited work

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Observation 46d37b7e-2c34-4dfb-96ab-ff29129c7173 · outbound

This paper cites Skeleton-in-context: Unified skeleton sequence modeling with in-context learning.

ConText: Driving In-context Learning for Text Removal and Segmentation Skeleton-in-context: Unified skeleton sequence modeling with in-context learning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.031264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04db03b1-f2d5-46aa-a672-37782a66dee2 · outbound

This paper cites What is the real need for scene text removal? exploring the background integrity and erasure exhaustivity properties.

ConText: Driving In-context Learning for Text Removal and Segmentation What is the real need for scene text removal? exploring the background integrity and erasure exhaustivity properties

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.016877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c30a8386-9b14-40a6-b31f-432896db86a7 · outbound

This paper cites In-context learning unlocked for diffusion models.

ConText: Driving In-context Learning for Text Removal and Segmentation In-context learning unlocked for diffusion models

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:16.002086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c71371b7-5122-4a45-b8ba-4d87b3af7f1c · outbound

This paper cites V., Zhou, D., et al.

ConText: Driving In-context Learning for Text Removal and Segmentation V., Zhou, D., et al

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:13.234361Z

Source-reported events for the cited work

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Observation 23be2a94-05bc-4577-93a9-c4c09e7b445d · outbound

This paper cites The learnability of in-context learning.

ConText: Driving In-context Learning for Text Removal and Segmentation The learnability of in-context learning

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:13.300492Z

Source-reported events for the cited work

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Observation 782be91a-a712-444e-962a-a39e46b10467 · outbound

This paper cites M., and Luo, P.

ConText: Driving In-context Learning for Text Removal and Segmentation M., and Luo, P

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.967977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a97127ab-8317-47c4-8eec-ba132b312267 · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

ConText: Driving In-context Learning for Text Removal and Segmentation An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:13.408248Z

Source-reported events for the cited work

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Observation 61b1ea9a-f27a-4742-961a-13d216a17e28 · outbound

This paper cites Rethinking text segmentation: A novel dataset and a text-specific refinement approach.

ConText: Driving In-context Learning for Text Removal and Segmentation Rethinking text segmentation: A novel dataset and a text-specific refinement approach

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.953919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d237d668-76fd-4d6c-ad9b-d7c1f271e80a · outbound

This paper cites Bts: a bi-lingual benchmark for text segmentation in the wild.

ConText: Driving In-context Learning for Text Removal and Segmentation Bts: a bi-lingual benchmark for text segmentation in the wild

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.939438Z

Source-reported events for the cited work

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Observation 7bdbc804-e51e-4b96-b563-656f0ac3c8b1 · outbound

This paper cites Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V.

ConText: Driving In-context Learning for Text Removal and Segmentation Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:13.646565Z

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Observation f0a3c0cd-0590-4bf7-8965-4c41978770b8 · outbound

This paper cites Multi-modal prototypes for open-world semantic segmentation.

ConText: Driving In-context Learning for Text Removal and Segmentation Multi-modal prototypes for open-world semantic segmentation

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.925027Z

Source-reported events for the cited work

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Observation e1ba47f6-40a8-4491-aa84-6737154ac828 · outbound

This paper cites Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation.

ConText: Driving In-context Learning for Text Removal and Segmentation Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

Reference 82

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:01:15.329321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8cb2c129-a8dd-4043-98bc-cb68cb97f5eb · outbound

This paper cites Scene text segmentation with text-focused transformers.

ConText: Driving In-context Learning for Text Removal and Segmentation Scene text segmentation with text-focused transformers

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.910258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd3baa7c-4b68-407c-b2bc-9145c35fe2ac · outbound

This paper cites Scene text segmentation with text-focused transformers.

ConText: Driving In-context Learning for Text Removal and Segmentation Scene text segmentation with text-focused transformers

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.895534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9a4f3461-6596-456c-9a4b-06ded375817e · outbound

This paper cites Eaformer: Scene text segmentation with edge-aware transformers.

ConText: Driving In-context Learning for Text Removal and Segmentation Eaformer: Scene text segmentation with edge-aware transformers

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.881014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2c2f1d6f-4611-49f7-8053-f79997112fb2 · outbound

This paper cites How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning.

ConText: Driving In-context Learning for Text Removal and Segmentation How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:14.267944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09ccf479-cb01-4785-a599-4603fccf417c · outbound

This paper cites and Nakayama, H.

ConText: Driving In-context Learning for Text Removal and Segmentation and Nakayama, H

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.866863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 953148b6-e5ca-455c-9fc0-e1f2e4b02b76 · outbound

This paper cites Choose what you need: Disentangled representation learning for scene text recognition removal and editing.

ConText: Driving In-context Learning for Text Removal and Segmentation Choose what you need: Disentangled representation learning for scene text recognition removal and editing

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.852329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b86683cd-20cd-47e2-bdcc-367acf1601a4 · outbound

This paper cites Complementary patch for weakly supervised semantic segmentation.

ConText: Driving In-context Learning for Text Removal and Segmentation Complementary patch for weakly supervised semantic segmentation

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.836737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d19b2e62-f8d9-44ed-b851-c9d34bd69755 · outbound

This paper cites Uncovering prototypical knowledge for weakly open-vocabulary semantic segmentation.

ConText: Driving In-context Learning for Text Removal and Segmentation Uncovering prototypical knowledge for weakly open-vocabulary semantic segmentation

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.822487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9521e2e5-3ca9-4a17-a126-fd798aa376f8 · outbound

This paper cites Instruct me more! random prompting for visual in-context learning.

ConText: Driving In-context Learning for Text Removal and Segmentation Instruct me more! random prompting for visual in-context learning

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.808030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e7999922-d830-4833-bb74-c1a9c96c0794 · outbound

This paper cites Ensnet: Ensconce text in the wild.

ConText: Driving In-context Learning for Text Removal and Segmentation Ensnet: Ensconce text in the wild

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.792756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a1323c7-e37a-4752-986f-cc94580c628e · outbound

This paper cites G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models.

ConText: Driving In-context Learning for Text Removal and Segmentation G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:15.034294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ade145f-f96b-414a-bcac-94e3b24066f8 · outbound

This paper cites What makes good examples for visual in-context learning? Advances in Neural Information Processing Systems, 36: 0 17773--17794, 2023 b.

ConText: Driving In-context Learning for Text Removal and Segmentation What makes good examples for visual in-context learning? Advances in Neural Information Processing Systems, 36: 0 17773--17794, 2023 b

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:01:15.777844Z

Source-reported events for the cited work

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Observation 08daf8ea-02e6-4406-bd0b-ec8d4f7b4ba3 · outbound

This paper cites Image Segmentation in Foundation Model Era: A Survey.

ConText: Driving In-context Learning for Text Removal and Segmentation Image Segmentation in Foundation Model Era: A Survey

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:15.203110Z

Source-reported events for the cited work

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Observation 4c31ef1c-ef25-419a-a80d-509da9640fd0 · outbound

This paper cites Visual Text Generation in the Wild.

ConText: Driving In-context Learning for Text Removal and Segmentation Visual Text Generation in the Wild

Reference 96

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:01:15.260251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a3c5911a-dcd5-4d27-8396-390902c27cde · outbound

This paper cites write newline.

ConText: Driving In-context Learning for Text Removal and Segmentation write newline

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:15.212230Z

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