Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation | Paper Citation Record

Observation 164c07b1-7686-4efc-b874-32d516a45982 · outbound

This paper cites L., and Parikh, D.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation L., and Parikh, D

Reference 1

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Observation c46b9bcc-11bd-4caf-934f-f96e6921968d · outbound

This paper cites Fast and inexpensive color image segmentation for interactive robots.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Fast and inexpensive color image segmentation for interactive robots

Reference 2

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Observation 46202bfd-1a10-452a-90e5-0590aea08908 · outbound

This paper cites Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors

Reference 3

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Observation f6ee7d9f-08e7-474c-95f3-9730ea7722c1 · outbound

This paper cites J., Elliot, S., and Cakmak, M.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation J., Elliot, S., and Cakmak, M

Reference 4

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Observation 0d125d09-e92e-4635-b20a-e5f93f073294 · outbound

This paper cites F., and Chen, C.-S.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation F., and Chen, C.-S

Reference 5

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Observation 36437f81-cb89-4973-8e70-cfb65aa6ef2a · outbound

This paper cites E., Stoica, I., and Xing, E.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation E., Stoica, I., and Xing, E

Reference 6

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Observation fae14050-a333-45e2-999f-e7c6829f9772 · outbound

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

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation The cityscapes dataset for semantic urban scene understanding

Reference 7

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Observation 6b18a60c-32cc-4549-8b46-6fdd43722770 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:39.930499Z

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Observation 694e7551-b4d1-4d85-b8d8-6c47506e68ac · outbound

This paper cites Vision-language transformer and query generation for referring segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Vision-language transformer and query generation for referring segmentation

Reference 9

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Observation 57be3e30-a2c5-487d-960d-37bbf40365a5 · outbound

This paper cites G., R \"u ckl \'e , A., Lee, J.-U., Schulz, C., Mesgar, M., Swarnkar, K., Simpson, E., and Gurevych, I.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation G., R \"u ckl \'e , A., Lee, J.-U., Schulz, C., Mesgar, M., Swarnkar, K., Simpson, E., and Gurevych, I

Reference 10

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Observation 1670ad9f-3429-470a-9b57-3ff3751a89aa · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 11

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Observation 02a63f5e-0f78-4b59-a75a-f11ea676da2b · outbound

This paper cites Vision meets robotics: The kitti dataset.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Vision meets robotics: The kitti dataset

Reference 12

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Observation f3e80f7c-6a19-42bd-9155-251bd31dbed0 · outbound

This paper cites Mask r-cnn.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Mask r-cnn

Reference 13

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Observation c38b6826-a6d3-490a-b109-9f40d85b7808 · outbound

This paper cites Prompt-to-prompt image editing with cross-attention control.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Prompt-to-prompt image editing with cross-attention control

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:55.694641Z

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Observation d570fb94-273e-4d45-9ac5-6644b102c7b2 · outbound

This paper cites Denoising diffusion probabilistic models.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Denoising diffusion probabilistic models

Reference 15

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Observation d4dbe620-9632-49e4-84db-df57166f7ade · outbound

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Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Unresolved cited work

Reference 16

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Observation 986e3372-5e18-4a66-82eb-6e2d161a667c · outbound

This paper cites Locate then segment: A strong pipeline for referring image segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Locate then segment: A strong pipeline for referring image segmentation

Reference 17

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Observation d0ff2736-7c77-4746-bd66-97904f57ccda · outbound

This paper cites Pnp inversion: Boosting diffusion-based editing with 3 lines of code.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Pnp inversion: Boosting diffusion-based editing with 3 lines of code

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:55.197011Z

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Observation 0da827f4-c3ba-4553-acbe-b05f1bb222dd · outbound

This paper cites Diffusion Models for Open-Vocabulary Segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Diffusion Models for Open-Vocabulary Segmentation

Reference 19

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Observation 4f3de432-c336-4158-8e3c-92a97f196b04 · outbound

This paper cites Diffusion models for open-vocabulary segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Diffusion models for open-vocabulary segmentation

Reference 20

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Observation 80c90ae7-c056-4d36-a147-1b634c0fbe17 · outbound

This paper cites Referitgame: Referring to objects in photographs of natural scenes.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Referitgame: Referring to objects in photographs of natural scenes

Reference 21

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Observation 22ff80f0-d67a-4d15-9250-a7018cd9628b · outbound

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Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Unresolved cited work

Reference 22

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Observation e11f0e4c-4f01-434b-b634-2af06771f681 · outbound

This paper cites C., Lo, W.-Y., et al.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation C., Lo, W.-Y., et al

Reference 23

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Observation 8475f94c-c1ee-446a-bb38-4cfac8cae99c · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Lisa: Reasoning segmentation via large language model

Reference 24

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Observation a49972b3-8d1c-4b99-a338-37359a2552cc · outbound

This paper cites Cryptext: Database and interactive toolkit of human-written text perturbations in the wild.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Cryptext: Database and interactive toolkit of human-written text perturbations in the wild

Reference 25

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Observation 972299a0-ed18-497b-89b0-be3c7b835187 · outbound

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Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Unresolved cited work

Reference 26

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Observation 3cf8b8dc-d271-4fc8-850d-4bd41317c138 · outbound

This paper cites Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:41.206735Z

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Observation 7518ac67-355f-4689-a22a-a61a9c585da1 · outbound

This paper cites E., Setio, A.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation E., Setio, A

Reference 28

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Observation c4109939-6268-492a-88d7-414efe5df704 · outbound

This paper cites Gres: Generalized referring expression segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Gres: Generalized referring expression segmentation

Reference 29

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Observation 3bf6c785-fcb3-4d53-95d1-4282daa41d7c · outbound

This paper cites Deep learning for generic object detection: A survey.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Deep learning for generic object detection: A survey

Reference 30

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Observation 10b715c3-fc37-4b71-a64d-7640aee9b4c8 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 31

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Observation 652472c8-474b-4753-a1de-2a83e38ddedd · outbound

This paper cites Fully convolutional networks for semantic segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Fully convolutional networks for semantic segmentation

Reference 32

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Observation 75c040e2-a40d-4120-b799-833a34e38477 · outbound

This paper cites H., Holynski, A., and Darrell, T.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation H., Holynski, A., and Darrell, T

Reference 33

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Observation 7bb6db2a-5bac-41a0-b096-2584fc55ce72 · outbound

This paper cites Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Segclip: Patch aggregation with learnable centers for open-vocabulary semantic segmentation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:53.715166Z

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Observation b1c80291-5dbe-45f2-a43f-69a408a8cb96 · outbound

This paper cites L., and Murphy, K.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation L., and Murphy, K

Reference 35

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Observation ddba789e-c2af-432c-90ff-912ec9d70b5e · outbound

This paper cites C., and Mart \' nez, J.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation C., and Mart \' nez, J

Reference 36

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Observation 99d6cad1-547a-4009-a95a-dbed58273c84 · outbound

This paper cites Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features

Reference 37

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This paper cites H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al

Reference 38

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Observation f5665f67-fa0a-4396-8850-0af178c317fa · outbound

This paper cites Null-text inversion for editing real images using guided diffusion models.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Null-text inversion for editing real images using guided diffusion models

Reference 39

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Observation a6f7d098-f03c-46d1-8ed8-dbd091166ff2 · outbound

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

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation The role of context for object detection and semantic segmentation in the wild

Reference 40

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Observation 5c0e4a78-6ffe-44ea-be4f-ac682f204e4a · outbound

This paper cites H., and Lim, S.-N.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation H., and Lim, S.-N

Reference 41

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Observation 26be77f2-4555-4a91-8b5e-162e1aac3179 · outbound

This paper cites EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models

Reference 42

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Observation ed39a8f4-a974-4d1a-8cdd-6839da7897e4 · outbound

This paper cites Localizing object-level shape variations with text-to-image diffusion models.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Localizing object-level shape variations with text-to-image diffusion models

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:52.467562Z

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Observation 3c73a9a2-46ef-4ad0-b05f-16ebfec2fa1c · outbound

This paper cites Kosmos-2: Grounding Multimodal Large Language Models to the World.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Kosmos-2: Grounding Multimodal Large Language Models to the World

Reference 44

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Observation 12dfb219-b998-4c26-9bf3-1959d104680a · outbound

This paper cites A., Wang, L., Cervantes, C.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation A., Wang, L., Cervantes, C

Reference 45

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Observation 327b48aa-b249-46c9-a074-2a7ea40a6544 · outbound

This paper cites Improving language understanding by generative pre-training.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Improving language understanding by generative pre-training

Reference 46

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Observation 8c4bfaa4-c64e-48ba-9ef7-b4af63ce6901 · outbound

This paper cites W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al

Reference 47

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Observation 9c15bbfc-e77f-4c7e-a2a4-594d01ce6b54 · outbound

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

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Zero-shot text-to-image generation

Reference 48

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Observation 83f09a2e-dc3e-4418-b394-03a5240541e2 · outbound

This paper cites Perceptual grouping in contrastive vision-language models.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Perceptual grouping in contrastive vision-language models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:52.227729Z

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Observation ef8e8d9d-977d-47ea-b1cc-916ad2bd0354 · outbound

This paper cites M., Xing, E., Yang, M.-H., and Khan, F.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation M., Xing, E., Yang, M.-H., and Khan, F

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:52.003447Z

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Observation 92665210-4fa2-48f6-b1e4-acfd008d9cf3 · outbound

This paper cites Hyper- SD : Trajectory segmented consistency model for efficient image synthesis.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Hyper- SD : Trajectory segmented consistency model for efficient image synthesis

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:51.780387Z

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Observation 669b7824-35b5-41c8-88a6-c73e73a44c57 · outbound

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

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation High-resolution image synthesis with latent diffusion models

Reference 52

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Observation 5567f5c1-07e2-40b4-bb02-0a708deed302 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 53

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Observation 715af51c-56c7-4afd-9dd1-96fdde0a77c3 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:51.562183Z

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Observation 187d319c-5ea0-44d9-bef1-9319e0740c68 · outbound

This paper cites Denoising diffusion implicit models.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Denoising diffusion implicit models

Reference 55

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Observation 126315e7-b894-4077-968f-4ddb9d1b0a03 · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

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Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:51.322040Z

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Observation f94b6865-32d8-422c-b2ff-4f3cb44ccb6d · outbound

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

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Clip as rnn: Segment countless visual concepts without training endeavor

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:51.086315Z

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Observation a936b286-2469-4a74-90da-b68a44b5717e · outbound

This paper cites What the daam: Interpreting stable diffusion using cross attention.

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Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:50.865025Z

Source-reported events for the cited work

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Observation 00fb7408-ae2f-4af1-9234-c7cc1e423c90 · outbound

This paper cites A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., and Li, L.-J.

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Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:50.676009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 015164fc-2247-44b1-b426-123853792d9e · outbound

This paper cites Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion

Reference 60

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Observation 125694d8-5278-426b-9217-fdbe57efbde4 · outbound

This paper cites Concept decomposition for visual exploration and inspiration.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Concept decomposition for visual exploration and inspiration

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:50.494116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 657a6a79-0594-4e79-9b2f-d5e06e5bfd6f · outbound

This paper cites Cris: Clip-driven referring image segmentation.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Cris: Clip-driven referring image segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:50.269955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 09f77ab3-b9ff-4a63-9f13-74f1297ee41a · outbound

This paper cites Towards open vocabulary learning: A survey.

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Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:49.999158Z

Source-reported events for the cited work

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Observation e9716017-9122-4aec-9a40-2c3f0e0404de · outbound

This paper cites Gan inversion: A survey.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Gan inversion: A survey

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:49.778516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 85b34c55-ccb9-4d25-8965-e7c3b5a73ec5 · outbound

This paper cites Gsva: Generalized segmentation via multimodal large language models.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Gsva: Generalized segmentation via multimodal large language models

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:49.567596Z

Source-reported events for the cited work

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Observation 5df82faf-f746-43e6-853f-1873e668f103 · outbound

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

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Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:49.330314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1e0304fd-1cf5-45bb-bd7b-59686629c9b6 · outbound

This paper cites Learning open-vocabulary semantic segmentation models from natural language supervision.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Learning open-vocabulary semantic segmentation models from natural language supervision

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:49.070645Z

Source-reported events for the cited work

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Observation 5dfa4d5a-4639-44b4-b9ac-b9c3bd8eea03 · outbound

This paper cites Open-vocabulary panoptic segmentation with text-to-image diffusion models.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Open-vocabulary panoptic segmentation with text-to-image diffusion models

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:48.850069Z

Source-reported events for the cited work

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Observation 38a957d1-add1-42ed-b6a6-fe1db5a1fae5 · outbound

This paper cites Bridging vision and language encoders: Parameter-efficient tuning for referring image segmentation.

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Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:48.554235Z

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Observation 3ac1dcbf-49d8-4a1e-8969-dd19384d2661 · outbound

This paper cites Z., Guo, Z., Zhou, K., Zhang, W., and Liu, Z.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Z., Guo, Z., Zhou, K., Zhang, W., and Liu, Z

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:48.367511Z

Source-reported events for the cited work

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Observation cbe3e587-878e-4879-b140-918281b60404 · outbound

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Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:42:48.131992Z

Source-reported events for the cited work

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Observation 8fb7ed89-0984-4c56-b5d8-9c6b0f84b6af · outbound

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Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Unresolved cited work

Reference 72

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:42:47.916065Z

Source-reported events for the cited work

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Observation 2c3ab9f2-f223-49f7-8ff6-6280c5123617 · outbound

This paper cites H., and Son, J.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation H., and Son, J

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:47.676038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 6e4587a7-680e-48c8-a5ee-96717b3c4406 · outbound

This paper cites OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:44.759272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87cf7b64-9d9d-4727-a4af-864edee51f22 · outbound

This paper cites Psalm: Pixelwise segmentation with large multi-modal model.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Psalm: Pixelwise segmentation with large multi-modal model

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:47.335621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 631c7900-249d-46af-8903-83abaf27ff11 · outbound

This paper cites Unleashing text-to-image diffusion models for visual perception.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Unleashing text-to-image diffusion models for visual perception

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:47.212319Z

Source-reported events for the cited work

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Observation d6a24a21-2330-46a2-a6a2-7f30727e9eed · outbound

This paper cites BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation BuboGPT: Enabling Visual Grounding in Multi-Modal LLMs

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:44.947498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9feb6fb8-1c72-4972-acac-bccea5705f4d · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:45.014331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c99c8ed-1525-493e-82f8-3dead6bc0132 · outbound

This paper cites C., and Dai, B.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation C., and Dai, B

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:47.068762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation df42c6c8-b549-4b21-9cca-9374d28764a4 · outbound

This paper cites C., and Dai, B.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation C., and Dai, B

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:46.860478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2d8bfd88-47f1-4e80-889c-85727f4b09c6 · outbound

This paper cites C., and Liu, Z.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation C., and Liu, Z

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:46.695132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 48ae3b2c-802d-49a7-ad6d-7108f9116c85 · outbound

This paper cites C., and Liu, Z.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation C., and Liu, Z

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:46.542312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e74b1880-523d-44e6-aedf-5380c4e18ad4 · outbound

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

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation Image Segmentation in Foundation Model Era: A Survey

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:45.331969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcca3cbc-8479-41f9-a70f-7e40a4eb38db · outbound

This paper cites and Chen, L.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation and Chen, L

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:46.407328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation decab247-a1a5-4141-a4a3-da2af3cbd595 · outbound

This paper cites a henb \.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation a henb \

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:42:46.248821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ae95435f-b83b-40ae-8401-b938f519fea6 · outbound

This paper cites write newline.

Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation write newline

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T14:42:45.538411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers