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Paper Citation Record · LEDGER

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP

As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.07135.

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

pith.paper-citation-record.v1
2607.07135 v2

Coverage vector

measured 33 of 33 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-07-14T15:51:57.429693Z

measured 33 of 33 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 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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Reference resolution

33 of 33 outbound references displayed

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Outbound references

Observation ac92bd8c-7059-4dcd-8d4c-100be0fa7b56 · outbound

This paper cites The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding

Reference 1

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Observation b6d92444-7ff6-4eda-816f-19113a66b8d5 · outbound

This paper cites In: Chaudhuri, K., Sugiyama, M.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: Chaudhuri, K., Sugiyama, M

Reference 2

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Observation a007ac14-b9b3-429c-8cc3-f555c58c8b5b · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 3

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Observation 12d98669-4703-47a3-bfb8-07adc6e94f61 · outbound

This paper cites In: Proc.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: Proc

Reference 4

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Observation 75129e31-3482-4b2b-a2e6-b8d14192cf7c · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Unresolved cited work

Reference 5

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Observation 9835073e-5122-4fd3-a9be-257f443d1f99 · outbound

This paper cites International Journal of Computer Vision111(1), 98–136 (Jan 2015).

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP International Journal of Computer Vision111(1), 98–136 (Jan 2015)

Reference 6

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Unresolved cited work

Reference 7

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Observation 8cc8815d-36c6-4d6c-8587-7a72000a6941 · outbound

This paper cites In: Proceedings of the Winter Conference on Applications of Computer Vision (WACV).

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: Proceedings of the Winter Conference on Applications of Computer Vision (WACV)

Reference 8

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Observation 6ac6bed8-78c4-49fc-9664-96197e436796 · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Unresolved cited work

Reference 9

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This paper cites In: The Thirty-eighth Annual Conference on Neural Information Processing Systems (2024), https://openreview.net/forum?id=nExI4FuKWD.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: The Thirty-eighth Annual Conference on Neural Information Processing Systems (2024), https://openreview.net/forum?id=nExI4FuKWD

Reference 10

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Observation 0ab2171d-3585-4caf-abcf-f7385b1af114 · outbound

This paper cites In: European Conference on Computer Vision.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: European Conference on Computer Vision

Reference 11

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This paper cites In: CVPR (2022).

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: CVPR (2022)

Reference 12

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Unresolved cited work

Reference 13

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This paper cites CREPE: Can Vision-Language Foundation Models Reason Compositionally?.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP CREPE: Can Vision-Language Foundation Models Reason Compositionally?

Reference 14

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Observation 7e36a2dd-05c6-4277-8236-71f5600a1cee · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP 02068 16 F

Reference 15

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Observation bfd9fdad-8a17-4906-86c4-a39fe9ca294e · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Sparse Text Generation

Reference 16

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Observation fdeae959-8e26-4ca6-b34f-774b6c873dd5 · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2014).

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2014)

Reference 17

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Observation b8e6b49a-5fea-4d39-8f1b-ac7a1915bb3e · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Sparse Sequence-to-Sequence Models

Reference 18

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Observation add2e408-66f8-4a4f-a2f9-9c6cf6edbb27 · outbound

This paper cites Refining CLIP's Spatial Awareness: A Visual-Centric Perspective.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Refining CLIP's Spatial Awareness: A Visual-Centric Perspective

Reference 19

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Observation 6c66253d-ea31-4abd-8861-e0ce8c948771 · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP ICML (2021)

Reference 20

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Observation 96d56fde-0d17-4a14-89de-2ab5c661be13 · outbound

This paper cites Perceptual Grouping in Contrastive Vision-Language Models.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Perceptual Grouping in Contrastive Vision-Language Models

Reference 21

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Observation 04e98a8f-7155-41c9-bc6c-c14977b55eae · outbound

This paper cites Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation

Reference 22

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Observation 0720ea1c-4fbb-4dbb-ac2f-1fc6056db84c · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Unresolved cited work

Reference 23

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Observation 4f24a196-1fc1-434b-9fb9-d7a0dc38c51b · outbound

This paper cites Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned

Reference 24

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Observation ecf2435e-9775-47b6-8ef4-e92d53dad321 · outbound

This paper cites SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference

Reference 25

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Observation a5e2d96e-839b-4c66-8d91-8236e72a6bec · outbound

This paper cites CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction

Reference 26

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Observation d9e20162-6e9d-4646-8a95-6e69b1c01097 · outbound

This paper cites In: CVPR (2022).

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP In: CVPR (2022)

Reference 27

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Observation 7d8bd39a-bf69-4d85-b141-f968ba167baa · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Unresolved cited work

Reference 28

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP When and why vision-language models behave like bags-of-words, and what to do about it?

Reference 29

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP Sigmoid Loss for Language Image Pre-Training

Reference 30

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Observation 7afc5517-19f4-43d8-86c1-000569b96145 · outbound

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Sparse Attention for Dense Open-Vocabulary Prediction in CLIP MultiMax: Sparse and Multi-Modal Attention Learning

Reference 32

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Observation 19d2bf2d-cc41-4785-aee4-b2553028fae5 · outbound

This paper cites arXiv (2026).https://doi.

Sparse Attention for Dense Open-Vocabulary Prediction in CLIP arXiv (2026).https://doi

Reference 33

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Pith citing papers

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