Paper Citation Record · LEDGER

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning

As of 12 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2501.10695.

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

pith.paper-citation-record.v1
2501.10695 v2

Coverage vector

measured 43 of 43 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation a4cfcf06-add1-4051-9bc2-d575ef6780ae · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning A causal view of compositional zero-shot recognition

Reference 1

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Observation 1368955d-2b82-487e-a922-f36707ffc5fa · outbound

This paper cites Inferring analogous attributes.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Inferring analogous attributes

Reference 5

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Observation 07f48755-cb78-4e3d-8101-b226a0b6b1a4 · outbound

This paper cites Discovering states and transformations in im- age collections.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Discovering states and transformations in im- age collections

Reference 13

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Observation bba77c56-9e7f-4c02-82db-92ca1c42eb5a · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Hierarchical visual primitive experts for compositional zero-shot learning

Reference 16

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Observation d9252280-a8a9-4417-b660-93eb18633787 · outbound

This paper cites Symmetry and group in attribute-object com- positions.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Symmetry and group in attribute-object com- positions

Reference 17

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Observation 8d9e1eac-329b-4f90-815e-f59cc2de05f1 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Distilled reverse attention network for open-world compositional zero-shot learning

Reference 18

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Observation e8c4320c-a30a-42ba-8742-24ef9cf6f2f1 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Context-based and diversity-driven specificity in composi- tional zero-shot learning

Reference 19

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Observation 4343c0fa-d19a-4999-b72b-c14a611f0647 · outbound

This paper cites Simple primitives with feasibility-and contextuality- dependence for open-world compositional zero-shot learn- ing.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Simple primitives with feasibility-and contextuality- dependence for open-world compositional zero-shot learn- ing

Reference 20

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Observation b1bfbe1d-6e7b-4717-8d5f-a95dd83c40a6 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Decomposed soft prompt guided fusion en- hancing for compositional zero-shot learning

Reference 21

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Observation 72ca2701-a01d-473d-932e-dd76c31abe0d · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning From red wine to red tomato: Composition with context

Reference 22

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Observation 523aa4e6-02f5-4b0f-8f6f-537bdccdd36a · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Attributes as operators: factorizing unseen attribute-object compositions

Reference 24

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Observation a3341ef9-06c2-41a8-9487-d22b1093f683 · outbound

This paper cites Learning to Compose Soft Prompts for Compositional Zero-Shot Learning.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Learning to Compose Soft Prompts for Compositional Zero-Shot Learning

Reference 26

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Observation 617e23ce-c3b6-405e-95c2-1910fe6cd67e · outbound

This paper cites Chils: Zero- shot image classification with hierarchical label sets.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Chils: Zero- shot image classification with hierarchical label sets

Reference 27

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Observation cdbb09aa-5287-4100-aaf1-0554d9cb8dae · outbound

This paper cites A hierarchical classification ant colony algorithm for predicting gene ontology terms.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning A hierarchical classification ant colony algorithm for predicting gene ontology terms

Reference 28

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Observation cb7f3fd9-b506-4dd1-9790-7260d5708b18 · outbound

This paper cites Glove: Global vec- tors for word representation.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Glove: Global vec- tors for word representation

Reference 30

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Observation d90a326b-24a4-49eb-b9c3-f70fcc378570 · outbound

This paper cites Learning transferable visual models from nat- ural language supervision.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Learning transferable visual models from nat- ural language supervision

Reference 31

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Observation 696f0b8e-1b10-4233-9ad2-df91a0e7e48c · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Independent prototype propagation for zero- shot compositionality

Reference 32

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Observation 018fa194-4115-4af5-84b1-aa51df325982 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Disentangling visual embeddings for at- tributes and objects

Reference 33

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This paper cites The graph neural network model.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning The graph neural network model

Reference 34

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Observation a24b9625-41b4-48dd-93a2-763d78ba0e58 · outbound

This paper cites Adversarial fine-grained com- position learning for unseen attribute-object recognition.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Adversarial fine-grained com- position learning for unseen attribute-object recognition

Reference 37

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Observation 8404a540-c7da-477c-8930-3ad35f3e5bd6 · outbound

This paper cites Prompting Large Pre-trained Vision-Language Models For Compositional Concept Learning.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Prompting Large Pre-trained Vision-Language Models For Compositional Concept Learning

Reference 38

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Observation 83e1807f-2e06-40f3-9171-d3fbb180d2f3 · outbound

This paper cites Detclipv3: Towards versatile generative open- vocabulary object detection.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Detclipv3: Towards versatile generative open- vocabulary object detection

Reference 39

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Observation bb75b9b0-ca78-4714-b2e1-f13cd6adfb04 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Fine-grained visual comparisons with local learning

Reference 40

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Observation 9001218b-bd71-4e40-878c-2729a8939397 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Learning in- variant visual representations for compositional zero-shot learning

Reference 41

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Observation 6dca1ebb-b1e6-4575-a82e-7ee2546f90bd · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Learning to prompt for vision-language models

Reference 42

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This paper cites B-CNN: Branch Convolutional Neural Network for Hierarchical Classification.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning B-CNN: Branch Convolutional Neural Network for Hierarchical Classification

Reference 43

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Observation ad38c87e-5f63-4913-a064-5c21b2dbeee6 · outbound

This paper cites Retrieval-augmented primitive representa- tions for compositional zero-shot learning.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Retrieval-augmented primitive representa- tions for compositional zero-shot learning

Reference 1991

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Observation a13591bb-18ae-4577-8c91-2f8fa313a20e · outbound

This paper cites Dual part discovery network for zero-shot learning.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Dual part discovery network for zero-shot learning

Reference 2004

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Observation d67e468e-a767-438f-b2d8-b61ee596269c · outbound

This paper cites An empirical study on large-scale multi-label text classification including few and zero-shot labels.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning An empirical study on large-scale multi-label text classification including few and zero-shot labels

Reference 2006

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Observation a42ef23e-ea8d-44c2-94d8-326b340fae77 · outbound

This paper cites A survey of hierarchical classification across different ap- plication domains.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning A survey of hierarchical classification across different ap- plication domains

Reference 2008

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Observation 83a15b8a-9b28-46b8-80fc-b831be94ce98 · outbound

This paper cites Compositional zero-shot learning using multi-branch graph convolution and cross-layer knowledge sharing.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Compositional zero-shot learning using multi-branch graph convolution and cross-layer knowledge sharing

Reference 2009

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Observation 872aff0f-67ab-4c05-999d-833124213eb8 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Learning conditional attributes for compositional zero-shot learning

Reference 2011

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Observation 786df120-10e0-43ca-b5d0-192b5123f9df · outbound

This paper cites Transferability vs.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Transferability vs

Reference 2014

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Observation 68a84a11-0990-47db-8452-8b7d8fdb6726 · outbound

This paper cites Adaptive mix- tures of local experts.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Adaptive mix- tures of local experts

Reference 2015

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Observation be28fcfd-3a1f-4c68-b6d4-6b5d731d4c66 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Learning graph embeddings for compositional zero-shot learning

Reference 2017

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Observation a5b114b6-decd-42f7-815a-9a2b3d8da01a · outbound

This paper cites Recognizing unseen attribute-object pair with generative model.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Recognizing unseen attribute-object pair with generative model

Reference 2018

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Observation 185c0d99-98f4-4e95-b3f9-3695d8ec16ba · outbound

This paper cites Hsva: Hierarchical semantic-visual adaptation for zero-shot learning.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Hsva: Hierarchical semantic-visual adaptation for zero-shot learning

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:07:16.984232Z

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Observation 8825fbe1-20a7-420c-a933-7e89648946cd · outbound

This paper cites Prompting language-informed distribution for compositional zero-shot learning.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Prompting language-informed distribution for compositional zero-shot learning

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:07:17.053475Z

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Observation b73ee8b1-094f-42f5-91de-479c1c4111f4 · outbound

This paper cites An online algorithm for hierarchical phoneme classification.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning An online algorithm for hierarchical phoneme classification

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:07:16.974106Z

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Observation 4bef54c4-e940-4fa3-b536-1c025342ca57 · outbound

This paper cites Learning attention as disentangler for composi- tional zero-shot learning.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Learning attention as disentangler for composi- tional zero-shot learning

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:07:16.951343Z

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Observation 0e0fb71a-b613-41bb-b5a9-140684c2c381 · outbound

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

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Troika: Multi-path cross-modal traction for compositional zero- shot learning

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:07:16.940515Z

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Observation 44cea127-53fc-49f9-8847-c5d93cbdc6a9 · outbound

This paper cites Procc: Progressive cross-primitive compatibility for open-world compositional zero-shot learning.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Procc: Progressive cross-primitive compatibility for open-world compositional zero-shot learning

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:07:16.929717Z

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Observation 1ede8e8d-a9d3-4be5-89fd-7cb9a246548f · outbound

This paper cites Analysis of representations for domain adaptation.

Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning Analysis of representations for domain adaptation

Reference 2025

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Source-reported events for the cited work

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Pith citing papers

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