Paper Citation Record · LEDGER

Object-level Self-Distillation for Vision Pretraining

As of 10 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2506.05409.

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

pith.paper-citation-record.v1
2506.05409 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:52:54.973978Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

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

Source: cited_works

Reference resolution

49 of 49 outbound references displayed

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verified fuzzy20
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External citation measurements

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Outbound references

Observation 0c4c77bd-525f-4379-b8ac-ad7ba49c8b22 · outbound

This paper cites Deep ViT Features as Dense Visual Descriptors.

Object-level Self-Distillation for Vision Pretraining Deep ViT Features as Dense Visual Descriptors

Reference 1

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Observation 8f4f1e59-bdc1-417a-94f1-0755f3bcd612 · outbound

This paper cites Self-supervised learning from images with a joint-embedding predictive architecture.

Object-level Self-Distillation for Vision Pretraining Self-supervised learning from images with a joint-embedding predictive architecture

Reference 2

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Observation 39f8aa84-736b-4159-b3c9-ac111e486596 · outbound

This paper cites Towards in-context scene understanding.

Object-level Self-Distillation for Vision Pretraining Towards in-context scene understanding

Reference 3

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Observation d601e02e-6555-4c2b-bed4-86e731552f7b · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

Object-level Self-Distillation for Vision Pretraining BEiT: BERT Pre-Training of Image Transformers

Reference 4

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Observation d4e66b74-3c82-41a3-91cd-9a3d2daea8e2 · outbound

This paper cites Are we done with ImageNet?.

Object-level Self-Distillation for Vision Pretraining Are we done with ImageNet?

Reference 5

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Observation fd8d6619-be2a-41c8-80f6-6679c54ededb · outbound

This paper cites MONet: Unsupervised Scene Decomposition and Representation.

Object-level Self-Distillation for Vision Pretraining MONet: Unsupervised Scene Decomposition and Representation

Reference 6

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Observation 3ab378cd-c2ec-4af3-aa9d-9ce881e0176d · outbound

This paper cites End-to-end object detection with transformers.

Object-level Self-Distillation for Vision Pretraining End-to-end object detection with transformers

Reference 7

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Observation 2bc2c9a5-7fa3-49c1-a992-c00bf875414b · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Object-level Self-Distillation for Vision Pretraining Emerging properties in self-supervised vision transformers

Reference 8

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Observation 9c82dc22-3163-4437-b965-6312223d0ff1 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Object-level Self-Distillation for Vision Pretraining A simple framework for contrastive learning of visual representations

Reference 9

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Observation 6299418c-e640-408f-9168-6fa5a6d2a5e3 · outbound

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

Object-level Self-Distillation for Vision Pretraining Imagenet: A large-scale hierarchical image database

Reference 10

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Observation 67f2652a-8366-43d9-8cec-7b9faaf0147f · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Object-level Self-Distillation for Vision Pretraining BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 11

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Observation 85f2dc7d-ebbe-4e57-8242-e868f51b76f3 · outbound

This paper cites On the transfer of object-centric representation learning.

Object-level Self-Distillation for Vision Pretraining On the transfer of object-centric representation learning

Reference 12

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Observation 263ed725-ad70-4fa0-bbb4-5400f5cb24b8 · outbound

This paper cites Attention over learned object embeddings enables complex visual reasoning.

Object-level Self-Distillation for Vision Pretraining Attention over learned object embeddings enables complex visual reasoning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:55.545518Z

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Observation 54bf1c76-00f2-4e1e-9baa-e2726dd3ca84 · outbound

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

Object-level Self-Distillation for Vision Pretraining An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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Observation d8b15774-5d64-403f-80cc-e47d18cce68e · outbound

This paper cites The pascal visual object classes challenge: A retrospective.

Object-level Self-Distillation for Vision Pretraining The pascal visual object classes challenge: A retrospective

Reference 15

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Observation 2662183d-8910-4f6d-b7c4-603567a3e4f7 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.

Object-level Self-Distillation for Vision Pretraining Bootstrap your own latent-a new approach to self-supervised learning

Reference 16

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Observation 72ff2598-229a-4858-af51-980ec1c33472 · outbound

This paper cites Unsupervised Semantic Segmentation by Distilling Feature Correspondences.

Object-level Self-Distillation for Vision Pretraining Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Reference 17

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Observation af5ffdda-990a-4b83-baf3-f15dbf0de28e · outbound

This paper cites Masked autoencoders are scalable vision learners.

Object-level Self-Distillation for Vision Pretraining Masked autoencoders are scalable vision learners

Reference 18

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Observation 1fb37551-0c9e-4e79-9c16-9b27a6ff328c · outbound

This paper cites Efficient visual pretraining with contrastive detection.

Object-level Self-Distillation for Vision Pretraining Efficient visual pretraining with contrastive detection

Reference 19

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Observation 808d2e15-e936-4218-8433-32d75d576558 · outbound

This paper cites Object discovery and representation networks.

Object-level Self-Distillation for Vision Pretraining Object discovery and representation networks

Reference 20

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Observation 218af056-abeb-4cd7-aa74-1ac8f2c8b639 · outbound

This paper cites Segment anything.

Object-level Self-Distillation for Vision Pretraining Segment anything

Reference 21

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Observation bfbcf340-dcd5-43b8-92d1-87691d22e3bd · outbound

This paper cites CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping.

Object-level Self-Distillation for Vision Pretraining CrIBo: Self-Supervised Learning via Cross-Image Object-Level Bootstrapping

Reference 22

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Observation 32f05046-d05f-442a-b0ca-db80917fbbd6 · outbound

This paper cites Microsoft coco: Common objects in context.

Object-level Self-Distillation for Vision Pretraining Microsoft coco: Common objects in context

Reference 23

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Observation dc097102-f3a6-4cac-bc94-2a7590b8a648 · outbound

This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.

Object-level Self-Distillation for Vision Pretraining Grounding dino: Marrying dino with grounded pre-training for open-set object detection

Reference 24

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Observation 77737830-6a7d-4300-a8ba-e13d6cac174d · outbound

This paper cites Object-centric learning with slot attention.

Object-level Self-Distillation for Vision Pretraining Object-centric learning with slot attention

Reference 25

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Observation 3090c0ee-6db9-4f8e-9af0-b3132d803359 · outbound

This paper cites Class-agnostic object detection with multi-modal transformer.

Object-level Self-Distillation for Vision Pretraining Class-agnostic object detection with multi-modal transformer

Reference 26

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Observation 4b8de29f-a972-4d86-b301-0374c377f368 · outbound

This paper cites Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models.

Object-level Self-Distillation for Vision Pretraining Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models

Reference 27

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Observation d30f897e-0ba8-4e5d-9208-a8839eadecd8 · outbound

This paper cites Unsupervised learning of dense visual representations.

Object-level Self-Distillation for Vision Pretraining Unsupervised learning of dense visual representations

Reference 28

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Observation 969274a8-d206-4e24-b87b-dc8e53d88812 · outbound

This paper cites Neural congealing: Aligning images to a joint semantic atlas.

Object-level Self-Distillation for Vision Pretraining Neural congealing: Aligning images to a joint semantic atlas

Reference 29

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Observation 861c41db-1346-4146-bde7-de33800adf3e · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Object-level Self-Distillation for Vision Pretraining DINOv2: Learning Robust Visual Features without Supervision

Reference 30

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Observation 027c8501-67b8-4c3c-ad2b-f0a534165b98 · outbound

This paper cites Improving language understanding by generative pre-training.(2018), 2018.

Object-level Self-Distillation for Vision Pretraining Improving language understanding by generative pre-training.(2018), 2018

Reference 31

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Observation 3b797630-d64c-402c-a2ca-98a1076a9a89 · outbound

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

Object-level Self-Distillation for Vision Pretraining Learning transferable visual models from natural language supervision

Reference 32

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Observation d89c2bf2-2718-4400-95ba-7d5a8618fc91 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Object-level Self-Distillation for Vision Pretraining SAM 2: Segment Anything in Images and Videos

Reference 33

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Observation 4cf3507e-0984-47a2-b9a9-dc5fd72da7f1 · outbound

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

Object-level Self-Distillation for Vision Pretraining Do imagenet classifiers generalize to imagenet? In International conference on machine learning, pages 5389--5400

Reference 34

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This paper cites You only look once: Unified, real-time object detection.

Object-level Self-Distillation for Vision Pretraining You only look once: Unified, real-time object detection

Reference 35

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Observation baf18923-4db5-43d0-af5b-28b6817b5366 · outbound

This paper cites Are We Done with Object-Centric Learning?.

Object-level Self-Distillation for Vision Pretraining Are We Done with Object-Centric Learning?

Reference 36

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Observation 1b30982e-6436-4d6c-927c-eb499cc00399 · outbound

This paper cites Bridging the Gap to Real-World Object-Centric Learning.

Object-level Self-Distillation for Vision Pretraining Bridging the Gap to Real-World Object-Centric Learning

Reference 37

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Observation 99228d01-3c03-42b2-b57d-d507dc5750ff · outbound

This paper cites Evaluating machine accuracy on imagenet.

Object-level Self-Distillation for Vision Pretraining Evaluating machine accuracy on imagenet

Reference 38

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Observation c37011a0-5ce3-4742-8d9c-072e910e3825 · outbound

This paper cites Croc: Cross-view online clustering for dense visual representation learning.

Object-level Self-Distillation for Vision Pretraining Croc: Cross-view online clustering for dense visual representation learning

Reference 39

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Observation e3432fdd-81b0-4249-ae5b-d1a5451d2af9 · outbound

This paper cites Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases.

Object-level Self-Distillation for Vision Pretraining Convnets and imagenet beyond accuracy: Understanding mistakes and uncovering biases

Reference 40

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Observation 72da9c3d-4061-4759-9807-9c8d44cef805 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.

Object-level Self-Distillation for Vision Pretraining Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Reference 41

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Observation 767d379c-3c0b-4cf9-a642-97cef43c6653 · outbound

This paper cites From imagenet to image classification: Contextualizing progress on benchmarks.

Object-level Self-Distillation for Vision Pretraining From imagenet to image classification: Contextualizing progress on benchmarks

Reference 42

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Observation 00c2909d-3221-4241-827c-b060f92fc604 · outbound

This paper cites Splicing vit features for semantic appearance transfer.

Object-level Self-Distillation for Vision Pretraining Splicing vit features for semantic appearance transfer

Reference 43

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Observation 06994615-4d3a-46b6-b97e-381d17e14ad5 · outbound

This paper cites Dense contrastive learning for self-supervised visual pre-training.

Object-level Self-Distillation for Vision Pretraining Dense contrastive learning for self-supervised visual pre-training

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:55.271415Z

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Observation 5b50c22f-d140-43ab-89bc-aa9cd0b8cb87 · outbound

This paper cites Self-supervised visual representation learning with semantic grouping.

Object-level Self-Distillation for Vision Pretraining Self-supervised visual representation learning with semantic grouping

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:55.258331Z

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Observation 2b237aeb-0368-412d-b3c6-afe5ac14ecd9 · outbound

This paper cites Unsupervised object-level representation learning from scene images.

Object-level Self-Distillation for Vision Pretraining Unsupervised object-level representation learning from scene images

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:55.244587Z

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Observation b57b8659-4670-4432-ae26-c2d605449ecf · outbound

This paper cites Re-labeling imagenet: from single to multi-labels, from global to localized labels.

Object-level Self-Distillation for Vision Pretraining Re-labeling imagenet: from single to multi-labels, from global to localized labels

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:55.230722Z

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Observation 562eab54-3c33-493d-8da8-fa3795e212ab · outbound

This paper cites Scene parsing through ade20k dataset.

Object-level Self-Distillation for Vision Pretraining Scene parsing through ade20k dataset

Reference 48

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Observation 18ca66a9-4989-4c00-b86e-31483b866a09 · outbound

This paper cites iBOT: Image BERT Pre-Training with Online Tokenizer.

Object-level Self-Distillation for Vision Pretraining iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 49

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Pith citing papers

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