Paper Citation Record · LEDGER

Learning Clustering-based Prototypes for Compositional Zero-shot Learning

As of 8 August 2026, this Paper Citation Record lists 93 of 93 outbound references and 4 inbound Pith citation observations for arXiv:2502.06501.

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

pith.paper-citation-record.v1
2502.06501 v2

Coverage vector

measured 93 of 93 reference resolution

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: arxiv_reference, observed 2026-05-18T12:21:21.367200Z

Reference resolution

93 of 93 outbound references displayed

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External citation measurements

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Outbound references

Observation 1ce788e7-d00a-498e-bf87-a8ff79ea1c7f · outbound

This paper cites Revealing the multi- dimensional mental representations of natural objects underlying human similarity judgements.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Revealing the multi- dimensional mental representations of natural objects underlying human similarity judgements

Reference 1

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Observation aae28f98-22e8-429b-815d-084ecd775f7c · outbound

This paper cites Aspects of the Theory of Syntax.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Aspects of the Theory of Syntax

Reference 2

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Observation 4068faa1-1465-405e-9ebe-ce9656eca9e3 · outbound

This paper cites Building machines that learn and think like people.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Building machines that learn and think like people

Reference 3

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Observation 3088cb28-022b-4e70-93d0-6677337c2e62 · outbound

This paper cites Learning to generalize to new compositions in image understanding.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning to generalize to new compositions in image understanding

Reference 4

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Observation ce4a4132-5e48-4ce7-9ef3-6ed8f6687d26 · outbound

This paper cites From red wine to red tomato: Composition with context.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning From red wine to red tomato: Composition with context

Reference 5

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Observation 974bfdee-12ec-4b7d-85d2-75fb43c61490 · outbound

This paper cites Simple primitives with feasibility-and contextuality-dependence for open-world compo- sitional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Simple primitives with feasibility-and contextuality-dependence for open-world compo- sitional zero-shot learning

Reference 6

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Observation 45d12ad9-7d38-42d7-9efb-4f14cead8ae9 · outbound

This paper cites Symmetry and group in attribute-object compositions.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Symmetry and group in attribute-object compositions

Reference 7

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Observation a33d3c88-766b-4a81-9e71-19fef2ae5f71 · outbound

This paper cites Hierarchical visual primi- tive experts for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Hierarchical visual primi- tive experts for compositional zero-shot learning

Reference 8

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Observation 58494b3d-d83c-4d73-ab12-42e48d5e2a28 · outbound

This paper cites Open world compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Open world compositional zero-shot learning

Reference 9

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This paper cites Leveraging sub-class discimination for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Leveraging sub-class discimination for compositional zero-shot learning

Reference 10

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Observation e7a971ee-32c8-431b-ae9f-91fe7f0b115e · outbound

This paper cites Revealing the proximate long-tail distribution in composi- tional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Revealing the proximate long-tail distribution in composi- tional zero-shot learning

Reference 11

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Observation 469fc1cf-c55d-4086-bfa4-2bb4add377ca · outbound

This paper cites Learning conditional attributes for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning conditional attributes for compositional zero-shot learning

Reference 12

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Observation 50885ebd-9ae4-4f1a-aa1c-5ac7206b59e8 · outbound

This paper cites Learning invariant visual representations for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning invariant visual representations for compositional zero-shot learning

Reference 13

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This paper cites Troika: Multi-path cross-modal traction for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Troika: Multi-path cross-modal traction for compositional zero-shot learning

Reference 14

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This paper cites Context-based and diversity-driven specificity in compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Context-based and diversity-driven specificity in compositional zero-shot learning

Reference 15

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This paper cites Prompting Language-Informed Distribution for Compositional Zero-Shot Learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Prompting Language-Informed Distribution for Compositional Zero-Shot Learning

Reference 16

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This paper cites Learning to compose soft prompts for composi- tional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning to compose soft prompts for composi- tional zero-shot learning

Reference 17

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This paper cites Decomposed soft prompt guided fusion enhancing for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Decomposed soft prompt guided fusion enhancing for compositional zero-shot learning

Reference 18

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Observation e2c6a93f-fd11-4447-a985-1ba1d54d07bf · outbound

This paper cites Learning transferable visual models from natural language supervision.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning transferable visual models from natural language supervision

Reference 19

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This paper cites Siamese contrastive embedding network for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Siamese contrastive embedding network for compositional zero-shot learning

Reference 20

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This paper cites Distilled reverse attention network for open-world compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Distilled reverse attention network for open-world compositional zero-shot learning

Reference 21

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This paper cites Independent prototype propagation for zero- shot compositionality.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Independent prototype propagation for zero- shot compositionality

Reference 22

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This paper cites Learning attention as disentangler for com- positional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning attention as disentangler for com- positional zero-shot learning

Reference 23

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This paper cites Retrieval-augmented primitive rep- resentations for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Retrieval-augmented primitive rep- resentations for compositional zero-shot learning

Reference 24

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Observation 886ce96a-c09d-40b4-a4f0-7fa315ef68c0 · outbound

This paper cites Generalized conditional gradient: analysis of convergence and applications.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Generalized conditional gradient: analysis of convergence and applications

Reference 25

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This paper cites Discovering states and transformations in image collections.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Discovering states and transformations in image collections

Reference 26

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This paper cites Fine-grained visual comparisons with local learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Fine-grained visual comparisons with local learning

Reference 27

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This paper cites Learning graph embeddings for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning graph embeddings for compositional zero-shot learning

Reference 28

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Observation 88c3f5db-55b4-4163-8651-c62eb9135f62 · outbound

This paper cites Attributes as operators: factorizing unseen attribute- object compositions.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Attributes as operators: factorizing unseen attribute- object compositions

Reference 29

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Observation 6a682f27-2694-4a65-aa8d-27d75a4b2676 · outbound

This paper cites Task- driven modular networks for zero-shot compositional learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Task- driven modular networks for zero-shot compositional learning

Reference 30

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This paper cites On leveraging variational graph embeddings for open world compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning On leveraging variational graph embeddings for open world compositional zero-shot learning

Reference 31

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This paper cites Learn- ing graph embeddings for open world compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learn- ing graph embeddings for open world compositional zero-shot learning

Reference 32

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This paper cites Learning attention propagation for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning attention propagation for compositional zero-shot learning

Reference 33

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This paper cites Disentangling visual embeddings for at- tributes and objects.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Disentangling visual embeddings for at- tributes and objects

Reference 34

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This paper cites Learning unseen concepts via hierarchical decomposition and composition.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Learning unseen concepts via hierarchical decomposition and composition

Reference 35

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This paper cites A causal view of compositional zero-shot recognition.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning A causal view of compositional zero-shot recognition

Reference 36

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 37

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

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Scaling up visual and vision-language representation learning with noisy text supervision

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Prompting Large Pre-trained Vision-Language Models For Compositional Concept Learning

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Case-based reasoning: Foundational issues, methodological variations, and system approaches

Reference 40

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This paper cites Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies

Reference 41

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Deep residual learning for image recognition

Reference 42

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Swin transformer: Hierarchical vision transformer using shifted windows

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Very deep convolutional networks for large-scale image recognition

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Nearest neighbor pattern classification

Reference 45

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Prototype selection for nearest neighbor classification: Taxonomy and empirical study

Reference 46

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Neighbour- hood components analysis

Reference 47

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Neighborhood preserving embedding

Reference 48

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Prototypical networks for few-shot learning

Reference 49

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Rethinking semantic segmentation: A prototype view

Reference 50

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This paper cites Clustering based point cloud representation learning for 3d analysis.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Clustering based point cloud representation learning for 3d analysis

Reference 51

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This paper cites Unified 3d segmenter as prototypical classifiers.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Unified 3d segmenter as prototypical classifiers

Reference 52

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This paper cites Clustering propagation for universal medical image segmentation.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Clustering propagation for universal medical image segmentation

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Clustseg: Clustering for universal segmentation

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Visual Knowledge in the Big Model Era: Retrospect and Prospect

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This paper cites Transductive few-shot learning with prototype-based label prop- agation by iterative graph refinement.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Transductive few-shot learning with prototype-based label prop- agation by iterative graph refinement

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This paper cites Attribute prototype network for zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Attribute prototype network for zero-shot learning

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This paper cites Dual progressive prototype network for generalized zero-shot learning.

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Visual-augmented dynamic semantic prototype for generative zero-shot learning

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This paper cites Protoclip: Prototypical contrastive language image pretraining.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Protoclip: Prototypical contrastive language image pretraining

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This paper cites Self-supervised visual feature learning with deep neural net- works: A survey.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Self-supervised visual feature learning with deep neural net- works: A survey

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This paper cites Self-supervised representation learning: Introduction, advances, and challenges.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Self-supervised representation learning: Introduction, advances, and challenges

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Gmmseg: Gaussian mixture based generative semantic segmentation models

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This paper cites Deep metric learning: A survey.Symmetry, 11(9):1066,.

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Classification is a Strong Baseline for Deep Metric Learning

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Neural clustering based visual represen- tation learning

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Clus- terfomer: clustering as a universal visual learner

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Self-labelling via simultaneous clustering and representation learning

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Parameter-efficient transfer learning for nlp

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Lora: Low-rank adaptation of large language models

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This paper cites Csot: Curriculum and structure-aware optimal transport for learning with noisy labels.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Csot: Curriculum and structure-aware optimal transport for learning with noisy labels

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This paper cites PLOT: Prompt Learning with Optimal Transport for Vision-Language Models.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Visual recognition with deep nearest centroids

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning Prototype-based semantic segmentation

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This paper cites Deep clustering for unsupervised learning of visual features.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Deep clustering for unsupervised learning of visual features

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This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Unsupervised learning of visual features by contrasting cluster assignments

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This paper cites An algorithm for restricted least squares regression.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning An algorithm for restricted least squares regression

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This paper cites Dual-stream contrastive learning for compositional zero-shot recognition.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Dual-stream contrastive learning for compositional zero-shot recognition

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Learning Clustering-based Prototypes for Compositional Zero-shot Learning A kernel statistical test of independence

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This paper cites Gipcol: Graph-injected soft prompting for compositional zero-shot learning.

Learning Clustering-based Prototypes for Compositional Zero-shot Learning Gipcol: Graph-injected soft prompting for compositional zero-shot learning

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This paper cites Adam: A Method for Stochastic Optimization.

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This paper cites On the translocation of masses.

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:19:06.626333Z

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

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Observation 8c472eef-79c7-4eaf-a02b-111fcb9c5c8f · outbound

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Observation f73c4e1a-35ed-476d-88b5-a72f9244ebb5 · outbound

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