Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation | Paper Citation Record

Observation 997a0ac5-7e69-411c-88e7-b4ef6e809179 · outbound

This paper cites Relevant intrinsic feature enhancement network for few-shot semantic segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Relevant intrinsic feature enhancement network for few-shot semantic segmentation

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This paper cites Few shot se- mantic segmentation: a review of methodologies and open challenges.arXiv e-prints, pages arXiv–2304,.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Few shot se- mantic segmentation: a review of methodologies and open challenges.arXiv e-prints, pages arXiv–2304,

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This paper cites Pixel matching network for cross- domain few-shot segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Pixel matching network for cross- domain few-shot segmentation

Reference 3

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This paper cites A transformer-based adaptive prototype matching network for few-shot semantic segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation A transformer-based adaptive prototype matching network for few-shot semantic segmentation

Reference 4

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Observation a0b7960d-8a0c-4125-8696-4b5dd95ed898 · outbound

This paper cites Query- guided prototype evolution network for few-shot seg- mentation.IEEE Transactions on Multimedia, 2024.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Query- guided prototype evolution network for few-shot seg- mentation.IEEE Transactions on Multimedia, 2024

Reference 5

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Observation 37b699af-0f91-421b-98c7-3e955dc8f900 · outbound

This paper cites Imagenet: A large-scale hierarchi- cal image database.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Imagenet: A large-scale hierarchi- cal image database

Reference 6

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

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation The pascal visual object classes (voc) challenge.Interna- tional journal of computer vision, 88:303–338, 2010

Reference 7

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Observation 9c7e67c8-10f4-41f5-b745-cb257f40754b · outbound

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

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Self-support few-shot semantic segmentation

Reference 8

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Observation 9c4db6f9-92e4-4b4a-8da1-feebf93f90e9 · outbound

This paper cites Semantic contours from inverse detectors.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Semantic contours from inverse detectors

Reference 9

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Observation 6d9bf538-2b8e-441c-be69-bae9858602a8 · outbound

This paper cites Deep residual learning for image recognition.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Deep residual learning for image recognition

Reference 10

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Observation 061322da-70e0-48a0-b0af-51b5f50d6e6e · outbound

This paper cites Prototype adaption and projection for few- and zero-shot 3d point cloud semantic segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Prototype adaption and projection for few- and zero-shot 3d point cloud semantic segmentation

Reference 11

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Observation e27d104b-71e5-4f06-8034-254b9e576b7d · outbound

This paper cites Cost aggregation with 4d con- volutional swin transformer for few-shot segmenta- tion.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Cost aggregation with 4d con- volutional swin transformer for few-shot segmenta- tion

Reference 12

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Observation 53cd2dec-fdbf-4a7d-ac33-9247f23fdaf9 · outbound

This paper cites Attention-based multi-context guiding for few-shot semantic segmen- tation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Attention-based multi-context guiding for few-shot semantic segmen- tation

Reference 13

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This paper cites Prototypical kernel learning and open-set foreground perception for generalized few-shot semantic segmen- tation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Prototypical kernel learning and open-set foreground perception for generalized few-shot semantic segmen- tation

Reference 14

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Observation d336d3ae-bacf-4ea1-a9ab-17eb4cd0e7b5 · outbound

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

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Learning what not to segment: A new perspec- tive on few-shot segmentation

Reference 15

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Observation 9b53e693-023d-439a-abc6-8a755e4ccc6c · outbound

This paper cites Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation

Reference 16

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This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks

Reference 17

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Observation b3ae2037-8ec4-4369-ac67-430a170d8505 · outbound

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

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Adaptive prototype learning and allocation for few-shot seg- mentation

Reference 18

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Observation e71c4df9-b1a9-4903-ab13-1c99ce8d9660 · outbound

This paper cites Microsoft coco: Com- mon objects in context.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Microsoft coco: Com- mon objects in context

Reference 19

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This paper cites Fecanet: Boost- ing few-shot semantic segmentation with feature- enhanced context-aware network.IEEE Transactions on Multimedia, 25:8580–8592, 2023.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Fecanet: Boost- ing few-shot semantic segmentation with feature- enhanced context-aware network.IEEE Transactions on Multimedia, 25:8580–8592, 2023

Reference 20

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Observation 3bc43ea6-360b-4118-b00c-067e557ad998 · outbound

This paper cites Dynamic proto- type convolution network for few-shot semantic seg- mentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Dynamic proto- type convolution network for few-shot semantic seg- mentation

Reference 21

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Observation 7a044d25-ccf6-44b7-a8e8-012a86755d43 · outbound

This paper cites Fully convolutional networks for semantic segmen- tation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Fully convolutional networks for semantic segmen- tation

Reference 22

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Observation a47c1879-289a-4581-9c93-0ebbcd6b2a18 · outbound

This paper cites Simpler is better: Few-shot semantic segmentation with classifier weight trans- former.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Simpler is better: Few-shot semantic segmentation with classifier weight trans- former

Reference 23

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Observation 23f015ad-09a7-4617-8319-343b36508783 · outbound

This paper cites Hyper- correlation squeeze for few-shot segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Hyper- correlation squeeze for few-shot segmentation

Reference 24

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This paper cites Feature weight- ing and boosting for few-shot segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Feature weight- ing and boosting for few-shot segmentation

Reference 25

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This paper cites Hierarchi- cal dense correlation distillation for few-shot segmen- tation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Hierarchi- cal dense correlation distillation for few-shot segmen- tation

Reference 26

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This paper cites Few-Shot Segmentation Propagation with Guided Networks.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Few-Shot Segmentation Propagation with Guided Networks

Reference 27

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This paper cites One-Shot Learning for Semantic Segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation One-Shot Learning for Semantic Segmentation

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This paper cites Dense cross-query-and-support attention weighted mask aggregation for few-shot segmenta- tion.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Dense cross-query-and-support attention weighted mask aggregation for few-shot segmenta- tion

Reference 29

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This paper cites A comprehensive survey of few-shot learning: Evolution, applications, chal- lenges, and opportunities.ACM Computing Surveys, 55(13s):1–40, 2023.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation A comprehensive survey of few-shot learning: Evolution, applications, chal- lenges, and opportunities.ACM Computing Surveys, 55(13s):1–40, 2023

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This paper cites Pixel-by-pixel cross-domain align- ment for few-shot semantic segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Pixel-by-pixel cross-domain align- ment for few-shot semantic segmentation

Reference 31

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This paper cites Prior guided feature enrichment network for few-shot segmenta- tion.IEEE transactions on pattern analysis and ma- chine intelligence, 44(2):1050–1065, 2020.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Prior guided feature enrichment network for few-shot segmenta- tion.IEEE transactions on pattern analysis and ma- chine intelligence, 44(2):1050–1065, 2020

Reference 32

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This paper cites Few-shot semantic segmentation with democratic attention net- works.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Few-shot semantic segmentation with democratic attention net- works

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This paper cites Rethinking prior informa- tion generation with clip for few-shot segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Rethinking prior informa- tion generation with clip for few-shot segmentation

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This paper cites Panet: Few-shot image seman- tic segmentation with prototype alignment.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Panet: Few-shot image seman- tic segmentation with prototype alignment

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This paper cites Rethink- ing the correlation in few-shot segmentation: A buoys view.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Rethink- ing the correlation in few-shot segmentation: A buoys view

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This paper cites Scale-aware graph neural network for few- shot semantic segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Scale-aware graph neural network for few- shot semantic segmentation

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Observation b0f73a16-5b74-421b-903f-a963080e39aa · outbound

This paper cites Doubly deformable aggregation of covariance matri- ces for few-shot segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Doubly deformable aggregation of covariance matri- ces for few-shot segmentation

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Observation 7a2932dc-9c66-4a25-a8bf-30e052928f65 · outbound

This paper cites Self-calibrated cross attention network for few-shot segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Self-calibrated cross attention network for few-shot segmentation

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Observation 7b81a580-ce93-4842-b884-9230c93339e8 · outbound

This paper cites Hybrid Mamba for Few-Shot Segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Hybrid Mamba for Few-Shot Segmentation

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Observation 7b3a83a6-0fa9-48e0-8eb8-5b573646194c · outbound

This paper cites Eliminating feature ambiguity for few-shot segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Eliminating feature ambiguity for few-shot segmentation

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:41.608496Z

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Observation 51f6223e-5388-4ee7-9b62-59078e824fb1 · outbound

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

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Prototype mixture models for few-shot semantic segmentation

Reference 42

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This paper cites BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation BriNet: Towards Bridging the Intra-class and Inter-class Gaps in One-Shot Segmentation

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Resolution: verified exact; local_arxiv, observed 2026-08-15T18:03:41.271320Z

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Observation e65cbe9b-3178-41f2-8a25-cfff55a0e1b0 · outbound

This paper cites Self- guided and cross-guided learning for few-shot seg- mentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Self- guided and cross-guided learning for few-shot seg- mentation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:41.578535Z

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Observation 0b5c3dde-222c-41de-9b9f-30bff1884c97 · outbound

This paper cites Few-shot segmentation via cycle-consistent transformer.Advances in Neural Information Process- ing Systems, 34:21984–21996, 2021.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Few-shot segmentation via cycle-consistent transformer.Advances in Neural Information Process- ing Systems, 34:21984–21996, 2021

Reference 45

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Observation 93c2eb5b-508c-47d7-8818-a6b9ec6034f2 · outbound

This paper cites Mask matching transformer for few-shot segmentation.Ad- vances in Neural Information Processing Systems, 35: 823–836, 2022.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Mask matching transformer for few-shot segmentation.Ad- vances in Neural Information Processing Systems, 35: 823–836, 2022

Reference 46

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Observation a4faa058-da97-4e4f-b9a6-94fd14aec3c3 · outbound

This paper cites Mfnet: Mul- ticlass few-shot segmentation network with pixel- wise metric learning.IEEE Transactions on Circuits and Systems for Video Technology, 32(12):8586–8598,.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Mfnet: Mul- ticlass few-shot segmentation network with pixel- wise metric learning.IEEE Transactions on Circuits and Systems for Video Technology, 32(12):8586–8598,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:41.388577Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 37a99e56-da08-45ee-9630-09645a2f11dd · outbound

This paper cites Sg-one: Similarity guidance network for one- shot semantic segmentation.IEEE transactions on cy- bernetics, 50(9):3855–3865, 2020.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Sg-one: Similarity guidance network for one- shot semantic segmentation.IEEE transactions on cy- bernetics, 50(9):3855–3865, 2020

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:41.375961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e62ee753-ef3c-4f9c-bb6c-80dc59731e29 · outbound

This paper cites Fgnet: Towards filling the intra-class and inter-class gaps for few-shot segmentation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Fgnet: Towards filling the intra-class and inter-class gaps for few-shot segmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:41.363353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc096a55-7414-4f34-889d-6c946a5d2fb1 · outbound

This paper cites Addressing background context bias in few-shot segmentation through iterative mod- ulation.

Balancing Conservatism and Aggressiveness: Prototype-Affinity Hybrid Network for Few-Shot Segmentation Addressing background context bias in few-shot segmentation through iterative mod- ulation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:41.350241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers