Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation | Paper Citation Record

Observation 7f564f01-5161-41bd-96c6-60a853dbb572 · outbound

This paper cites Loss functions in the era of semantic segmentation: A survey and outlook, 2023.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Loss functions in the era of semantic segmentation: A survey and outlook, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.169949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 647a6ef4-9f69-4651-94a4-f9a0d0f5833d · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Exploring Visual Prompts for Adapting Large-Scale Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.580426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85012c3a-ca0d-4736-9981-8f16e1c5bcb6 · outbound

This paper cites Zero-shot semantic segmentation.Advances in Neural Information Processing Systems, 32, 2019.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Zero-shot semantic segmentation.Advances in Neural Information Processing Systems, 32, 2019

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.149694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e82906dd-cb71-4d1c-b38b-360a1af5905c · outbound

This paper cites Nu- cleus segmentation across imaging experiments: the 2018 data science bowl.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Nu- cleus segmentation across imaging experiments: the 2018 data science bowl

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.133604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f4f518b0-0006-474c-ad3d-333f0ad58352 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.117232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9939afd5-f34c-417f-9bc7-4658f5dac835 · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.600208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50b09159-3b4c-46e1-bc86-7fc3cf920985 · outbound

This paper cites Causal invariance as an essential constraint for creating representation of the world: generalizing the invariance of causal power.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Causal invariance as an essential constraint for creating representation of the world: generalizing the invariance of causal power

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.100684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ecc2e744-d1c1-416a-9cb5-5a3b2b9a4691 · outbound

This paper cites Segment and Track Anything.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Segment and Track Anything

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.610308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0e8db3a-d12d-401d-a431-f7179163eb10 · outbound

This paper cites Semantics segmentation of car parts.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Semantics segmentation of car parts

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.084790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f9649b1e-3566-4037-9caa-18594f4bb00d · outbound

This paper cites Coconut: Modernizing coco segmenta- tion.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Coconut: Modernizing coco segmenta- tion

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.069701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6d365008-fd45-4c26-8b53-d85befc90242 · outbound

This paper cites Deep multi-modal struc- tural equations for causal effect estimation with unstructured proxies.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Deep multi-modal struc- tural equations for causal effect estimation with unstructured proxies

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.054198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 60ea3fd8-4f5d-4d72-937f-cdd8375a69ce · outbound

This paper cites De- coupling zero-shot semantic segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation De- coupling zero-shot semantic segmentation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.038627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 71b37d23-9f8b-4756-9edd-e38428b3d4f7 · outbound

This paper cites Maskclip: Masked self- distillation advances contrastive language-image pretraining.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Maskclip: Masked self- distillation advances contrastive language-image pretraining

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.634271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19384b7c-f46c-4779-aaaf-b61a64f665ea · outbound

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

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.639157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b60cd9d3-4a88-498b-9fcd-c9a3081f865f · outbound

This paper cites Learning to prompt for open-vocabulary ob- ject detection with vision-language model.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Learning to prompt for open-vocabulary ob- ject detection with vision-language model

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.643656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76aacf2a-bff2-42fc-bc44-cc1f2302bf57 · outbound

This paper cites Self- support few-shot semantic segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Self- support few-shot semantic segmentation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:21.003354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8ff8da85-107b-45b7-9e89-ab4f1678cb07 · outbound

This paper cites Learning to recognize objects in egocentric activities.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Learning to recognize objects in egocentric activities

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.987996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 31904fcc-fb22-4e1e-8cc5-0dbd4e266706 · outbound

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

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Scal- ing open-vocabulary image segmentation with image-level labels

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.972340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d4674a40-0f2c-40d6-a884-b1a8295c10e6 · outbound

This paper cites Clip and complementary methods.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Clip and complementary methods

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.955413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 16ebc333-2969-4f6a-90a6-b2a01f085450 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Masked autoencoders are scalable vision learners

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.666433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb0b5e07-d40c-483f-a526-b845106a781b · outbound

This paper cites TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.671299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12ad7cbc-9588-47a2-87f8-cdef5b4d4b0b · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Parameter-efficient transfer learning for nlp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.931362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 42e2dc4a-67b9-4a82-9707-9647db1806f3 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation LoRA: Low-Rank Adaptation of Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.681394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 421b135b-672a-4309-9376-90106d5e8d3d · outbound

This paper cites Causal inference for leveraging image-text matching bias in multi-modal fake news detection.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Causal inference for leveraging image-text matching bias in multi-modal fake news detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.916484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 66015d7f-e941-43ac-ba10-c051d2bad357 · outbound

This paper cites Kvasir-seg: A segmented polyp dataset.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Kvasir-seg: A segmented polyp dataset

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.901489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ff0fa06a-2557-40b5-b0f6-72a197da2cea · outbound

This paper cites Vi- sual prompt tuning.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Vi- sual prompt tuning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.696142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af4141ac-4a0f-4bce-b631-695e6091c309 · outbound

This paper cites Learning mask-aware clip representations for zero-shot segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Learning mask-aware clip representations for zero-shot segmentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.700869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 779b9882-3c3d-49ae-b5f3-96f4df27c8bf · outbound

This paper cites Maple: Multi-modal prompt learning.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Maple: Multi-modal prompt learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.705666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ae91c10-b28f-4a2d-9819-a214fd752612 · outbound

This paper cites Segment any- thing.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Segment any- thing

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.857586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3135caa1-7b67-431b-884c-f35f61a156b5 · outbound

This paper cites Maskgan: Towards diverse and interactive facial image ma- nipulation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Maskgan: Towards diverse and interactive facial image ma- nipulation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.715822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcf855c5-9c02-43b2-8fa5-33faab10d7cf · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.720385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a1719b4-761f-4c89-b598-9bc6168e097c · outbound

This paper cites Language-driven Semantic Segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Language-driven Semantic Segmentation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.725110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92544ab2-28bf-498f-98f3-f5e309dbc33a · outbound

This paper cites An underwater image enhancement benchmark dataset and beyond.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation An underwater image enhancement benchmark dataset and beyond

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.832157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e166efc8-a633-4b16-8ca2-7be582f26a97 · outbound

This paper cites SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.734490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33d49e72-a10e-42b5-808f-2652dcfee332 · outbound

This paper cites ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.739469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b1f1f6b-d6c2-4dd1-9be3-f821e7add788 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.744102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb6a3f05-139b-42a3-aa93-a12b7d371e0a · outbound

This paper cites Exploring plain vision transformer backbones for object de- tection.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Exploring plain vision transformer backbones for object de- tection

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.817160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c1e31777-bda7-4fb3-b4d8-fa8b7b04b384 · outbound

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

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Open-vocabulary semantic segmentation with mask-adapted clip

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.802334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 229b8424-3245-47fd-95ba-fadee64fb2c6 · outbound

This paper cites Microsoft coco: Common objects in context.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Microsoft coco: Common objects in context

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.786824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c68c5dcd-52fc-4c0e-97ed-0840da990556 · outbound

This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in nat- ural language processing.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Pre-train, prompt, and predict: A systematic survey of prompting methods in nat- ural language processing

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.762375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a58269c-bcf6-4184-a4e0-b97660173096 · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.767120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72b0654e-1dd2-4d8a-9abe-a3875cb5ac48 · outbound

This paper cites Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.771751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb43834-e3fd-43d6-80c7-f835e2d19f9e · outbound

This paper cites Empower Vision Applications with LoRA LMM.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Empower Vision Applications with LoRA LMM

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.776217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f218864-755b-4d90-aa16-ef03294319fb · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Efficient Estimation of Word Representations in Vector Space

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.780641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c53f2d46-84fd-43d9-b3c0-8e6ea73f98b8 · outbound

This paper cites Image Segmentation Using Deep Learning: A Survey.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Image Segmentation Using Deep Learning: A Survey

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.785371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e1f6860-d2a2-447a-a818-3f93767b7954 · outbound

This paper cites Foundations of machine learning, 2018.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Foundations of machine learning, 2018

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.751338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 92a6770d-5742-47a2-a055-6ba9cdb054f0 · outbound

This paper cites Causality.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Causality

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.736290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2480defc-6070-4895-8bfa-45596d5619b3 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Learning transferable visual models from natural language supervi- sion

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.798955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fb60d34-a91a-42f7-a59c-cc7ce24c8809 · outbound

This paper cites Human segmentation dataset - tiktok dances.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Human segmentation dataset - tiktok dances

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.711317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d0b7ec22-891a-47d9-a342-f97005daf288 · outbound

This paper cites an unresolved cited work.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:45:20.695964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6d8c07ee-0e7d-49f7-8bc7-4f0a1a58d393 · outbound

This paper cites Re- viving iterative training with mask guidance for interactive segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Re- viving iterative training with mask guidance for interactive segmentation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.681600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aa0fe25e-e516-4378-ae10-c6716e304b8f · outbound

This paper cites 3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous Driving.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation 3D Annotation-Free Learning by Distilling 2D Open-Vocabulary Segmentation Models for Autonomous Driving

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:45:20.067157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 16cb5b28-f32f-40a7-85b2-2037bf4f8b58 · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimen- sional domains.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Fourier features let networks learn high frequency functions in low dimen- sional domains

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.666286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d40350e9-c198-4fa1-bdc5-a8b367235dab · outbound

This paper cites A feature-integration theory of attention.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation A feature-integration theory of attention

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.651187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f4ab1008-d10a-4210-b283-dc1513b7dc59 · outbound

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

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Sam-clip: Merging vision foundation models to- wards semantic and spatial understanding

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.635163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0955d217-3b03-4348-92dd-d07a3286e42b · outbound

This paper cites Amsa: Adaptive multimodal learning for senti- ment analysis.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Amsa: Adaptive multimodal learning for senti- ment analysis

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.617469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6e61c0aa-d24a-42ac-b181-a509771117d3 · outbound

This paper cites Hacking Task Confounder in Meta-Learning.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Hacking Task Confounder in Meta-Learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.840697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6e9626a-fe7f-4934-b135-22c23197f7f1 · outbound

This paper cites Towards the causal complete cause of multi-modal representation learning, 2025.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Towards the causal complete cause of multi-modal representation learning, 2025

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.602206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1eea3edb-0c3e-43a8-b86e-5261f0213b00 · outbound

This paper cites Exploring cross-image pixel contrast for semantic segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Exploring cross-image pixel contrast for semantic segmentation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.587111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b4078cbd-8d30-44ff-984f-58b296c23c0a · outbound

This paper cites Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.854526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f68992dd-85c6-4800-a8bc-828a48d4afcb · outbound

This paper cites To- wards open vocabulary learning: A survey.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation To- wards open vocabulary learning: A survey

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.569895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ec3be448-3a40-4a39-b138-19988b57ea93 · outbound

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

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Semantic projection network for zero-and few-label semantic segmentation

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.554064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dc604528-2d15-4371-8158-5c112aed7c57 · outbound

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

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Groupvit: Semantic segmentation emerges from text supervision

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.868352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e7571d2-4a3f-4a59-91ec-cae87cb4382c · outbound

This paper cites Side adapter network for open-vocabulary semantic segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Side adapter network for open-vocabulary semantic segmentation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.528119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 83fa8398-e168-40c5-b842-3efbe067f9a7 · outbound

This paper cites Attentive mask clip.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Attentive mask clip

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.512857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3ab5cee3-295f-404a-93b1-a2dac9f7b0a6 · outbound

This paper cites Limo: Less is more for reasoning, 2025.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Limo: Less is more for reasoning, 2025

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.497521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 19eef83b-d1c8-43a2-8c37-c6d1299f2904 · outbound

This paper cites Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.885988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7b70419-c3d0-41cc-b986-87c85ee07826 · outbound

This paper cites A simple framework for open-vocabulary segmentation and detection.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation A simple framework for open-vocabulary segmentation and detection

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.482539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 12afb3a9-ddf0-492b-83a2-13d25ebbd3b8 · outbound

This paper cites Explor- ing the role of token in transformer-based time series fore- casting, 2024.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Explor- ing the role of token in transformer-based time series fore- casting, 2024

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.466137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c3de7614-9fc9-4e3a-be3c-402e51d51081 · outbound

This paper cites Blo-sam: Bi-level optimization based finetuning of the segment anything model for overfitting- preventing semantic segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Blo-sam: Bi-level optimization based finetuning of the segment anything model for overfitting- preventing semantic segmentation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.447900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 15ee78cc-ebe4-4abf-932a-9ba358ebd3cf · outbound

This paper cites Personalize Segment Anything Model with One Shot.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Personalize Segment Anything Model with One Shot

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.904299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2af174a-fba1-46f9-8a00-4ea68b3b10b1 · outbound

This paper cites Hsnet: A hybrid semantic network for polyp segmentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Hsnet: A hybrid semantic network for polyp segmentation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.432562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1f25f65b-1894-460f-9aa5-f0a175281909 · outbound

This paper cites Contrastive learning for label efficient semantic seg- mentation.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Contrastive learning for label efficient semantic seg- mentation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.415695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cd5ed686-f2fc-4069-b252-13e65cdb9365 · outbound

This paper cites Extract free dense labels from clip.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Extract free dense labels from clip

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.398080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e6a51e60-8612-4f99-b62a-46913508a8bf · outbound

This paper cites Lima: Less is more for alignment, 2023.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Lima: Less is more for alignment, 2023

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.382541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e329dc4d-e814-4128-8be6-2489d2a54697 · outbound

This paper cites Learning to prompt for vision-language models.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Learning to prompt for vision-language models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:19.928107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07b3c889-0d72-4fa0-ad8c-3eb119fcc95f · outbound

This paper cites Fig- ure 7 shows part of the deep-sea images.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation Fig- ure 7 shows part of the deep-sea images

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.341739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fed41cb3-a835-408a-ab7d-9fbf9d48bade · outbound

This paper cites The causal prompts are gen- erated by feeding each sample into the trained CaPL for reweighting.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation The causal prompts are gen- erated by feeding each sample into the trained CaPL for reweighting

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.326884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ecb2be18-0371-42d3-9855-3a1df2527f9f · outbound

This paper cites It is organized into several sections: • Appendix A provides a specific definition of the causal prompt, a proof of the theoretical analysis of the main text.

Causal Prompt Calibration Guided Segment Anything Model for Open-Vocabulary Multi-Entity Segmentation It is organized into several sections: • Appendix A provides a specific definition of the causal prompt, a proof of the theoretical analysis of the main text

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:20.357058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers