Paper Citation Record · LEDGER

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers

As of 13 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2411.14789.

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

pith.paper-citation-record.v1
2411.14789 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T14:59:52.281615Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy18
unresolved28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3667f728-116f-4d70-8df0-b86563989a6b · outbound

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

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Learning transferable visual models from natural language supervision

Reference 1

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Observation 7f3b4057-6abf-4e5c-ba0f-6635cd45248c · outbound

This paper cites Mobileclip: Fast image-text models through multi-modal reinforced training.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Mobileclip: Fast image-text models through multi-modal reinforced training

Reference 2

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Observation bd51ce5b-e1d3-47b9-939d-baad2c37c84a · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 3

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Observation 95ba7f40-9837-4a6a-96f4-48f3644cb71e · outbound

This paper cites FILIP: Fine-grained Interactive Language-Image Pre-Training.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers FILIP: Fine-grained Interactive Language-Image Pre-Training

Reference 4

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Observation 772f5775-3f49-4cc9-96ca-86ee78d549a0 · outbound

This paper cites Slip: Self-supervision meets language-image pre-training.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Slip: Self-supervision meets language-image pre-training

Reference 5

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Observation 9e9d7ef6-4019-4fdb-ad6a-63b163c52140 · outbound

This paper cites Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

Reference 6

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Observation deefb527-540a-4655-a01e-204eb4de7628 · outbound

This paper cites Unified contrastive learning in image-text-label space.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Unified contrastive learning in image-text-label space

Reference 7

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Observation befa4a88-7088-4800-977f-9293a0e45170 · outbound

This paper cites Sigmoid loss for language image pre- training.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Sigmoid loss for language image pre- training

Reference 8

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Observation d224ba9a-6194-4892-b457-f70d39b92c84 · outbound

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

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 9

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Observation d2113434-c7b6-416a-b61d-d75c170e37ad · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Distilling the Knowledge in a Neural Network

Reference 10

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Observation 0e814fee-c16d-467d-a4cc-56dc16025f96 · outbound

This paper cites Tinyclip: Clip distillation via affinity mimicking and weight inheritance.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Tinyclip: Clip distillation via affinity mimicking and weight inheritance

Reference 11

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Observation d09a2960-9757-4414-b699-6877ec18ba32 · outbound

This paper cites Clip-kd: An empirical study of clip model distillation.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Clip-kd: An empirical study of clip model distillation

Reference 12

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Observation 06934296-ab32-4eb8-921d-1b70242d165a · outbound

This paper cites Data Filtering Networks.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Data Filtering Networks

Reference 13

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Source-reported events for the cited work

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Observation 6da76b83-de18-419b-b00f-2480161d7073 · outbound

This paper cites Datacomp: In search of the next generation of multimodal datasets.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Datacomp: In search of the next generation of multimodal datasets

Reference 14

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Observation 0046f2d9-6ffa-40b8-8898-93617acc3956 · outbound

This paper cites Alip: Adaptive language-image pre-training with synthetic caption.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Alip: Adaptive language-image pre-training with synthetic caption

Reference 15

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Observation 99f775da-c713-4249-abd7-54cd86e75a8d · outbound

This paper cites VeCLIP: Improving CLIP Training via Visual-enriched Captions.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers VeCLIP: Improving CLIP Training via Visual-enriched Captions

Reference 16

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Source-reported events for the cited work

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Observation 36e15436-1244-43c3-8391-60b30af1edca · outbound

This paper cites Metaformer is actually what you need for vision.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Metaformer is actually what you need for vision

Reference 17

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Observation c8118bb7-c484-474a-83da-f2b90090f27f · outbound

This paper cites Deeply supervised salient object detection with short connections.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Deeply supervised salient object detection with short connections

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:52.668728Z

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Observation ecfb34f2-c296-4364-86e2-a67e3bae9621 · outbound

This paper cites Cascaded partial decoder for fast and accurate salient object detection.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Cascaded partial decoder for fast and accurate salient object detection

Reference 19

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Observation ec24a1d5-3abc-4585-ae60-f988163ab6d0 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

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Observation fd94dcaf-8fd9-41bc-acb9-11a1dab9fa50 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Swin transformer: Hierarchical vision transformer using shifted windows

Reference 21

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Observation abbf80c6-af9b-4da1-b80a-8f4a4d48a0d3 · outbound

This paper cites Less is more: Pay less attention in vision transformers.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Less is more: Pay less attention in vision transformers

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:52.637839Z

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Observation fd86234b-74ed-4a34-821a-ad2ef9365662 · outbound

This paper cites Cmt: Convolutional neural networks meet vision transformers.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Cmt: Convolutional neural networks meet vision transformers

Reference 23

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Observation ddca9952-036c-4d8c-8fc8-ac33cd6bcebb · outbound

This paper cites Fastvit: A fast hybrid vision transformer using structural reparameterization.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Fastvit: A fast hybrid vision transformer using structural reparameterization

Reference 24

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Observation 97a4c6e0-62c2-4894-9c15-4ad64edd5671 · outbound

This paper cites Universal Transformers.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Universal Transformers

Reference 25

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Observation e5f31a3b-c752-4246-8d4a-651867b554eb · outbound

This paper cites Perceiver: General perception with iterative attention.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Perceiver: General perception with iterative attention

Reference 26

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Observation 645b9ae5-35a5-4397-b4ef-4028d0cb4d43 · outbound

This paper cites Sharing low rank conformer weights for tiny always-on ambient speech recognition models.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Sharing low rank conformer weights for tiny always-on ambient speech recognition models

Reference 27

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Observation de702da3-e804-42f5-9342-b6696ed0b4fb · outbound

This paper cites Conformer: Convolution-augmented Transformer for Speech Recognition.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Conformer: Convolution-augmented Transformer for Speech Recognition

Reference 28

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Observation 19bc353d-c96a-4333-9622-b5e048a40494 · outbound

This paper cites Simplifying transformer blocks.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Simplifying transformer blocks

Reference 29

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Observation 26350de0-be99-4fa9-aadd-01befde4bec1 · outbound

This paper cites Attention is all you need.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Attention is all you need

Reference 30

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Observation 011605a2-487f-4b23-815c-7495e8f36552 · outbound

This paper cites The shaped transformer: Attention models in the infinite depth-and-width limit.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers The shaped transformer: Attention models in the infinite depth-and-width limit

Reference 31

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Observation 5d5940fc-69ea-4bae-852d-4586dd2e2c14 · outbound

This paper cites Augmenting Self-attention with Persistent Memory.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Augmenting Self-attention with Persistent Memory

Reference 32

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Observation d4744f33-043d-4ad0-b297-e92d696d6a8e · outbound

This paper cites Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory

Reference 33

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Observation 4a2d84b0-0353-4449-84eb-a5c79032403e · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers LoRA: Low-Rank Adaptation of Large Language Models

Reference 34

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Observation a43af1cc-e3a0-4340-9a0b-9d5d8037fd69 · outbound

This paper cites CoCa: Contrastive Captioners are Image-Text Foundation Models.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers CoCa: Contrastive Captioners are Image-Text Foundation Models

Reference 35

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Observation 57d172d9-f745-4b17-acf7-c7d30bbe706b · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural Information Processing Systems, 35:25278–25294, 2022.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in Neural Information Processing Systems, 35:25278–25294, 2022

Reference 36

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This paper cites Rils: Masked visual reconstruction in language semantic space.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Rils: Masked visual reconstruction in language semantic space

Reference 37

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Observation 8e900ed7-dc7f-4e8d-b091-74c8b1ba186c · outbound

This paper cites Extract free dense labels from clip.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Extract free dense labels from clip

Reference 38

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Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Openclip repository, 2021

Reference 39

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

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Imagenet: A large-scale hierarchical image database

Reference 40

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Observation 4241ff8b-9f35-40f9-bf3d-38bb0188fd6d · outbound

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

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pages 5389–5400

Reference 41

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

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 42

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This paper cites Learning robust global representations by penalizing local predictive power.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Learning robust global representations by penalizing local predictive power

Reference 43

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Source-reported events for the cited work

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Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Microsoft coco: Common objects in context

Reference 44

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This paper cites Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models

Reference 45

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This paper cites Randaugment: Practical automated data augmentation with a reduced search space.

Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers Randaugment: Practical automated data augmentation with a reduced search space

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T14:59:52.452649Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.