Paper Citation Record · LEDGER

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation

As of 23 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2506.22979.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.22979 v1

Coverage vector

measured 50 of 50 reference resolution

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measured 51 of 51 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:46:32.150239Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T05:46:32.650439Z

Reference resolution

50 of 50 outbound references displayed

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External citation measurements

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Outbound references

Observation 8c8040b9-d3b3-48d2-93ea-8e871b891808 · outbound

This paper cites Ex- ploiting a joint embedding space for generalized zero-shot semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Ex- ploiting a joint embedding space for generalized zero-shot semantic segmentation

Reference 1

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Observation 7e4e9509-d4a8-4fc1-90a1-796078e53ba3 · outbound

This paper cites Effective conditioned and composed im- age retrieval combining clip-based features.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Effective conditioned and composed im- age retrieval combining clip-based features

Reference 2

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Observation 36d79421-282c-42d1-a914-8baa4a4ff2d4 · outbound

This paper cites Prototype-based incre- mental few-shot semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Prototype-based incre- mental few-shot semantic segmentation

Reference 3

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Observation 9d5aeed8-6007-48a3-9eec-38f411869dc5 · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 4

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Observation c32736f2-6baa-4c93-a817-51ea4e8f1731 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Masked-attention mask transformer for universal image segmentation

Reference 5

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Observation 3b54b179-0f84-40c0-861a-a29e398a7763 · outbound

This paper cites Bayesian prompt learn- ing for image-language model generalization.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Bayesian prompt learn- ing for image-language model generalization

Reference 6

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Observation 59991e56-d050-465e-be88-e4c6c1023130 · outbound

This paper cites Few-shot semantic segmen- tation with prototype learning.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Few-shot semantic segmen- tation with prototype learning

Reference 7

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Observation 1147d728-2704-42a9-ac1d-021ba8736e4d · outbound

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

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation The pascal visual object classes (voc) challenge

Reference 8

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Observation ae7a563b-33c8-4f75-82c0-250fc403dd77 · outbound

This paper cites A strong baseline for generalized few-shot semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation A strong baseline for generalized few-shot semantic segmentation

Reference 9

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Observation 6ba75f50-d2c3-486c-ad20-a28df5aa9ff5 · outbound

This paper cites Visual prompting for generalized few- shot segmentation: A multi-scale approach.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Visual prompting for generalized few- shot segmentation: A multi-scale approach

Reference 10

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Observation 5e56a7ad-05ef-4ee5-a968-9b03df8e089c · outbound

This paper cites Semivl: semi- supervised semantic segmentation with vision-language guidance.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Semivl: semi- supervised semantic segmentation with vision-language guidance

Reference 11

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Observation aad14c3c-6957-4850-b2f5-5156d081a5e0 · outbound

This paper cites Prototypical Kernel Learning and Open-set Foreground Perception for Generalized Few-shot Semantic Segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Prototypical Kernel Learning and Open-set Foreground Perception for Generalized Few-shot Semantic Segmentation

Reference 12

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Observation 9d9bc71d-f6f6-4653-92e9-11fc58ef8678 · outbound

This paper cites CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models

Reference 13

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Observation 514df284-baa3-4c44-9f92-854516b57c67 · outbound

This paper cites Scaling up visual and vision-language representa- tion learning with noisy text supervision.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Scaling up visual and vision-language representa- tion learning with noisy text supervision

Reference 14

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This paper cites Vi- sual prompt tuning.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Vi- sual prompt tuning

Reference 15

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Observation 68ddcea1-c1ce-4c70-aa39-b343354655d8 · outbound

This paper cites Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering

Reference 16

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Observation 1b6a01ed-dd39-4fb3-89cd-49c75770f274 · outbound

This paper cites Varia- tional dropout and the local reparameterization trick.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Varia- tional dropout and the local reparameterization trick

Reference 17

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This paper cites Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

Reference 18

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This paper cites Probabilistic prompt learning for dense prediction, 2023.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Probabilistic prompt learning for dense prediction, 2023

Reference 19

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Observation 1f20581c-4322-4824-bbc5-779f9a454006 · outbound

This paper cites Learning what not to segment: A new perspective on few- shot segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Learning what not to segment: A new perspective on few- shot segmentation

Reference 20

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This paper cites VisualBERT: A Simple and Performant Baseline for Vision and Language.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 21

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This paper cites Microsoft coco: Common objects in context.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Microsoft coco: Common objects in context

Reference 22

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This paper cites Dynamic prototype convolu- tion network for few-shot semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Dynamic prototype convolu- tion network for few-shot semantic segmentation

Reference 23

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Observation a8de1ef2-34e8-4c50-8b56-05ede42dc962 · outbound

This paper cites Clip-driven universal model for organ segmentation and tumor detection.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Clip-driven universal model for organ segmentation and tumor detection

Reference 24

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This paper cites Learning orthogonal pro- totypes for generalized few-shot semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Learning orthogonal pro- totypes for generalized few-shot semantic segmentation

Reference 25

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This paper cites Inter- mediate prototype mining transformer for few-shot semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Inter- mediate prototype mining transformer for few-shot semantic segmentation

Reference 26

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Observation 8a115b15-bf83-41c8-84b7-27de111b6074 · outbound

This paper cites Image retrieval on real-life images with pre- trained vision-and-language models.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Image retrieval on real-life images with pre- trained vision-and-language models

Reference 27

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Observation 84051677-e1c5-4f56-b4f3-c5c3f534e310 · outbound

This paper cites Hypercorrela- tion squeeze for few-shot segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Hypercorrela- tion squeeze for few-shot segmentation

Reference 28

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This paper cites Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuning

Reference 29

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Observation c0bad3b8-f844-4026-96c4-35f312046bc8 · outbound

This paper cites ifs-rcnn: An incre- mental few-shot instance segmenter.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation ifs-rcnn: An incre- mental few-shot instance segmenter

Reference 30

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Observation 6aeabac7-ac2b-4aec-a4b5-6452a970fa51 · outbound

This paper cites Hierarchical dense cor- relation distillation for few-shot segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Hierarchical dense cor- relation distillation for few-shot segmentation

Reference 31

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

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Learning transferable visual models from natural language supervi- sion

Reference 32

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This paper cites Denseclip: Language-guided dense prediction with context- aware prompting.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Denseclip: Language-guided dense prediction with context- aware prompting

Reference 33

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This paper cites A sur- prisingly simple approach to generalized few-shot semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation A sur- prisingly simple approach to generalized few-shot semantic segmentation

Reference 34

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This paper cites One-shot learning for semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation One-shot learning for semantic segmentation

Reference 35

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This paper cites Prototypical networks for few-shot learning.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Prototypical networks for few-shot learning

Reference 36

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This paper cites Prior guided feature enrich- ment network for few-shot segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence , 44(2):1050– 1065, 2020.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Prior guided feature enrich- ment network for few-shot segmentation.IEEE Transactions on Pattern Analysis and Machine Intelligence , 44(2):1050– 1065, 2020

Reference 37

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Observation 7e705f4d-d906-4809-b7f5-b43a6b5cbf9a · outbound

This paper cites Generalized few-shot se- mantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Generalized few-shot se- mantic segmentation

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:52.180730Z

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Observation c8e6ee38-b0fc-433c-97b6-dbe787f68d65 · outbound

This paper cites Rethinking prior information genera- tion with clip for few-shot segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Rethinking prior information genera- tion with clip for few-shot segmentation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:52.042988Z

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This paper cites Panet: Few-shot image semantic seg- mentation with prototype alignment.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Panet: Few-shot image semantic seg- mentation with prototype alignment

Reference 40

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

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Cris: Clip- driven referring image segmentation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:51.774457Z

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Observation 6bf97e23-4da3-4ea9-b1cc-c81d6da926f5 · outbound

This paper cites Prototype mixture models for few-shot semantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Prototype mixture models for few-shot semantic segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:51.606950Z

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Observation cba22164-29ea-4828-aa85-6cbb51e5f876 · outbound

This paper cites Multi-Modal Prototypes for Open-World Semantic Segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Multi-Modal Prototypes for Open-World Semantic Segmentation

Reference 43

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

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation

Reference 44

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This paper cites Canet: Class-agnostic segmentation networks with it- erative refinement and attentive few-shot learning.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Canet: Class-agnostic segmentation networks with it- erative refinement and attentive few-shot learning

Reference 45

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Observation dc0ad2f7-b757-4320-a682-09a1ae2fd0d1 · outbound

This paper cites Exploring regional clues in clip for zero-shot semantic seg- mentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Exploring regional clues in clip for zero-shot semantic seg- mentation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:51.298971Z

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Observation 33723ef2-42c9-452b-a48f-0e318dbcbbd8 · outbound

This paper cites Pyramid scene parsing network.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Pyramid scene parsing network

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:51.218465Z

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Observation c5721af1-6e23-4616-b623-51074df19a15 · outbound

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

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Learning to prompt for vision-language models

Reference 48

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Observation 3b063eb8-62f1-48be-9dec-bf6f9478ba69 · outbound

This paper cites Zegclip: Towards adapting clip for zero-shot se- mantic segmentation.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Zegclip: Towards adapting clip for zero-shot se- mantic segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:59:51.114028Z

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Observation 6a3e7bd7-98ba-43ce-8d9e-be43bc2a9150 · outbound

This paper cites Unlocking the potential of pre-trained vision transform- ers for few-shot semantic segmentation through relationship descriptors.

Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation Unlocking the potential of pre-trained vision transform- ers for few-shot semantic segmentation through relationship descriptors

Reference 50

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Pith citing papers

Observation 91bf670f-bce6-4652-90e3-20f31677b143 · inbound

Multi-Cache Enhanced Prototype Learning for Test-Time Generalization of Vision-Language Models cites this paper.

Multi-Cache Enhanced Prototype Learning for Test-Time Generalization of Vision-Language Models Probabilistic Prototype Calibration of Vision-Language Models for Generalized Few-shot Semantic Segmentation

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:46:32.655258Z

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