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Paper Citation Record · LEDGER

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.13805.

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

pith.paper-citation-record.v1
2607.13805 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:43:48.168194Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

21 of 21 outbound references displayed

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Outbound references

Observation c11ce84f-ef54-46e3-989c-079037953f96 · outbound

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

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Learning transferable visual models from natural language supervision

Reference 1

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Observation c8a1fa36-0312-4351-8e10-2436800a70b6 · outbound

This paper cites Alpha-clip: A clip model focusing on wherever you want.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Alpha-clip: A clip model focusing on wherever you want

Reference 2

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Observation 6004c158-cea7-4310-87c7-88860db48557 · outbound

This paper cites Iaa: Inner-adaptor archi- tecture empowers frozen large language model with multimodal capabilities.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Iaa: Inner-adaptor archi- tecture empowers frozen large language model with multimodal capabilities

Reference 3

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Observation 4dc8c90b-feea-47d4-b2fa-a6336432dec1 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 4

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Observation beead931-ced8-4319-b077-1e09a9b3ba75 · outbound

This paper cites Cyclip: Cyclic contrastive language-image pretraining.Advances in Neural Information Processing Systems, 35:6704–6719, 2022.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Cyclip: Cyclic contrastive language-image pretraining.Advances in Neural Information Processing Systems, 35:6704–6719, 2022

Reference 5

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Observation abf0cccf-3e92-4cdc-8c0f-a6136dac2011 · outbound

This paper cites Geodesic multi-modal mixup for robust fine- tuning.Advances in Neural Information Processing Systems, 36:52326–52341, 2023.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Geodesic multi-modal mixup for robust fine- tuning.Advances in Neural Information Processing Systems, 36:52326–52341, 2023

Reference 6

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Observation 750a4242-5b57-4f73-8d9d-a17420ec62a4 · outbound

This paper cites Sus-x: Training-free name-only transfer of vision-language models.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Sus-x: Training-free name-only transfer of vision-language models

Reference 7

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Observation 69dd2210-958a-4178-a98a-44a02a5a27c4 · outbound

This paper cites Understanding and constructing latent modality structures in multi-modal representation learning.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Understanding and constructing latent modality structures in multi-modal representation learning

Reference 8

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Observation b21e126c-c892-4099-aaee-3e4bbc1b0100 · outbound

This paper cites Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Mitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP

Reference 9

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Observation 638d44d3-5364-4180-b6bf-c4a6319ab41d · outbound

This paper cites Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models

Reference 10

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Observation d4d99ae3-f221-41a5-9c81-fe0b5b126ddd · outbound

This paper cites Modfinity: Unsupervised domain adaptation with multimodal in- formation flow intertwining.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Modfinity: Unsupervised domain adaptation with multimodal in- formation flow intertwining

Reference 11

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Observation f82672b7-5a8a-4ef7-b44c-1b7f3a4c5981 · outbound

This paper cites Smartclip: Modular vision-language alignment with identification guarantees.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Smartclip: Modular vision-language alignment with identification guarantees

Reference 12

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Observation 1c8fb0d5-f796-4a99-bd10-2980cc108545 · outbound

This paper cites Aligning information capacity between vision and language via dense-to-sparse feature dis- tillation for image-text matching.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Aligning information capacity between vision and language via dense-to-sparse feature dis- tillation for image-text matching

Reference 13

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Observation ed5c2b76-c5ea-4902-ad74-fb5da4067826 · outbound

This paper cites Simcse: Simple contrastive learning of sentence embeddings.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Simcse: Simple contrastive learning of sentence embeddings

Reference 14

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Observation 9d7c0468-ef16-4e52-a98e-8d11016eea06 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Representation Learning with Contrastive Predictive Coding

Reference 15

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Observation ccc06aea-7021-402b-ae8c-cb3ab9d4d65e · outbound

This paper cites Mode: Clip data experts via clustering.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Mode: Clip data experts via clustering

Reference 16

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Observation 48b2a5e8-758e-45b5-b33f-e8f2484f1b55 · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image cap- tioning.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image cap- tioning

Reference 17

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Observation af6b02f3-d50f-46e3-8a95-8a987e0ee331 · outbound

This paper cites Softclip: Softer cross-modal alignment makes clip stronger.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Softclip: Softer cross-modal alignment makes clip stronger

Reference 18

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Observation 7b4066fb-fc62-4b11-94d7-d53ef1d71cbe · outbound

This paper cites Do imagenet classifiers generalize to imagenet? InInternational conference on machine learning, pages 5389–5400.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Do imagenet classifiers generalize to imagenet? InInternational conference on machine learning, pages 5389–5400

Reference 19

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Observation 94fe1aaa-acc3-4879-83f2-b7ae2f5d0f9e · outbound

This paper cites Learning robust global representations by penalizing local predictive power.Advances in neural information processing systems, 32, 2019.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization Learning robust global representations by penalizing local predictive power.Advances in neural information processing systems, 32, 2019

Reference 20

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Observation fc4eaa0a-5e1e-4ef8-90d7-4505428f200c · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

AspectCLIP: Optimizing CLIP Representation Space via Aspect-Guided Consistency Regularization The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 21

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Pith citing papers

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