Probing the Mid-level Vision Capabilities of Self-Supervised Learning | Paper Citation Record

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This paper cites Self-labelling via simultaneous clustering and rep- resentation learning, 2020.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Self-labelling via simultaneous clustering and rep- resentation learning, 2020

Reference 1

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This paper cites Es- timating and exploiting the aleatoric uncertainty in surface normal estimation, 2021.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Es- timating and exploiting the aleatoric uncertainty in surface normal estimation, 2021

Reference 2

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This paper cites Deep clustering for unsupervised learning of visual features, 2019.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Deep clustering for unsupervised learning of visual features, 2019

Reference 3

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This paper cites Unsupervised learning of visual features by contrasting cluster assignments, 2021.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Unsupervised learning of visual features by contrasting cluster assignments, 2021

Reference 4

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Emerg- ing properties in self-supervised vision transformers

Reference 5

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

Probing the Mid-level Vision Capabilities of Self-Supervised Learning A simple framework for contrastive learning of visual representations

Reference 6

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This paper cites Exploring simple siamese rep- resentation learning.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Exploring simple siamese rep- resentation learning

Reference 7

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This paper cites Improved Baselines with Momentum Contrastive Learning.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Improved Baselines with Momentum Contrastive Learning

Reference 8

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This paper cites An empirical study of training self-supervised vision transformers, 2021.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning An empirical study of training self-supervised vision transformers, 2021

Reference 9

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This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 10

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This paper cites Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner

Reference 11

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

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Imagenet: A large-scale hierarchical image database

Reference 12

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This paper cites Maskclip: Masked self-distillation advances contrastive language-image pretraining, 2023.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Maskclip: Masked self-distillation advances contrastive language-image pretraining, 2023

Reference 13

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

Probing the Mid-level Vision Capabilities of Self-Supervised Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 14

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This paper cites Depth map prediction from a single image using a multi-scale deep net- work.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Depth map prediction from a single image using a multi-scale deep net- work

Reference 15

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This paper cites Depth map prediction from a single image using a multi-scale deep net- work, 2014.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Depth map prediction from a single image using a multi-scale deep net- work, 2014

Reference 16

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This paper cites Prob- ing the 3d awareness of visual foundation models.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Prob- ing the 3d awareness of visual foundation models

Reference 17

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This paper cites Hospedales.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Hospedales

Reference 18

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Observation 6588d35b-6915-41e1-b989-7e49164e55f3 · outbound

This paper cites Everingham, L.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Everingham, L

Reference 19

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This paper cites Everingham, L.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Everingham, L

Reference 20

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This paper cites Adabins: Depth estimation using adaptive bins.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Adabins: Depth estimation using adaptive bins

Reference 21

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This paper cites Fouhey, Wajahat Hussain, Abhinav Gupta, and Mar- tial Hebert.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Fouhey, Wajahat Hussain, Abhinav Gupta, and Mar- tial Hebert

Reference 22

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This paper cites Dream- sim: Learning new dimensions of human visual similarity using synthetic data, 2023.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Dream- sim: Learning new dimensions of human visual similarity using synthetic data, 2023

Reference 23

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This paper cites Dream- sim: Learning new dimensions of human visual similarity using synthetic data.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Dream- sim: Learning new dimensions of human visual similarity using synthetic data

Reference 24

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This paper cites Un- supervised representation learning by predicting image rota- tions.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Un- supervised representation learning by predicting image rota- tions

Reference 25

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This paper cites Scaling and benchmarking self-supervised visual rep- resentation learning.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Scaling and benchmarking self-supervised visual rep- resentation learning

Reference 26

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This paper cites Caltech-256 object category dataset.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Caltech-256 object category dataset

Reference 27

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This paper cites Richemond, Elena Buchatskaya, Carl Do- ersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R´emi Munos, and Michal Valko.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Richemond, Elena Buchatskaya, Carl Do- ersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Moham- mad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R´emi Munos, and Michal Valko

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This paper cites Deep residual learning for image recognition.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Deep residual learning for image recognition

Reference 29

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This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Momentum contrast for unsupervised visual rep- resentation learning

Reference 30

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This paper cites Masked autoencoders are scalable vision learners, 2021.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Masked autoencoders are scalable vision learners, 2021

Reference 31

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This paper cites Masked autoencoders are scalable vision learners.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Masked autoencoders are scalable vision learners

Reference 32

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This paper cites Visual intelligence: How we create what we see.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Visual intelligence: How we create what we see

Reference 33

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This paper cites Navi: Category- agnostic image collections with high-quality 3d shape and pose annotations.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Navi: Category- agnostic image collections with high-quality 3d shape and pose annotations

Reference 34

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This paper cites Segment any- thing.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Segment any- thing

Reference 35

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This paper cites Microsoft COCO: Common Objects in Context.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Microsoft COCO: Common Objects in Context

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This paper cites Feature pyra- mid networks for object detection.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Feature pyra- mid networks for object detection

Reference 37

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This paper cites Pixmim: Rethinking pixel reconstruction in masked image modeling, 2023.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Pixmim: Rethinking pixel reconstruction in masked image modeling, 2023

Reference 38

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Probing the Mid-level Vision Capabilities of Self-Supervised Learning Unresolved cited work

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This paper cites The three r’s of computer vision: Recognition, recon- struction and reorganization.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning The three r’s of computer vision: Recognition, recon- struction and reorganization

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This paper cites A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning A Closer Look at Benchmarking Self-Supervised Pre-training with Image Classification

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This paper cites Vision: A computational investigation into the human representation and processing of visual information.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Vision: A computational investigation into the human representation and processing of visual information

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This paper cites Self-supervised learning of pretext-invariant representations.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Self-supervised learning of pretext-invariant representations

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This paper cites Indoor segmentation and support inference from rgbd images.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Indoor segmentation and support inference from rgbd images

Reference 44

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This paper cites How useful is self- supervised pretraining for visual tasks? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning How useful is self- supervised pretraining for visual tasks? In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

Reference 45

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This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Unsupervised learning of visual representations by solving jigsaw puzzles

Reference 46

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Probing the Mid-level Vision Capabilities of Self-Supervised Learning Unresolved cited work

Reference 47

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This paper cites Context encoders: Feature learning by inpainting.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Context encoders: Feature learning by inpainting

Reference 48

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This paper cites Beit v2: Masked image modeling with vector-quantized visual tokenizers, 2022.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Beit v2: Masked image modeling with vector-quantized visual tokenizers, 2022

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This paper cites idisc: Internal discretization for monocular depth estimation.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning idisc: Internal discretization for monocular depth estimation

Reference 50

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This paper cites Learn- ing transferable visual models from natural language super- vision.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Learn- ing transferable visual models from natural language super- vision

Reference 51

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This paper cites Vi- sion transformers for dense prediction.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Vi- sion transformers for dense prediction

Reference 52

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This paper cites Vi- sion transformers for dense prediction.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Vi- sion transformers for dense prediction

Reference 53

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Observation 5395ae1a-f9a3-4edd-be47-c33f3d3c0d1c · outbound

This paper cites Berg, and Li Fei-Fei.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Berg, and Li Fei-Fei

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.275872Z

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Observation 4646621b-2b2b-4d80-92cf-da42181f81e7 · outbound

This paper cites A Realistic Evaluation of Semi-supervised Learning for Fine- grained Classification.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning A Realistic Evaluation of Semi-supervised Learning for Fine- grained Classification

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.260466Z

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Observation caf40125-4546-46ae-b200-a60859afc461 · outbound

This paper cites Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs

Reference 56

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Observation 04cc993d-d815-420e-b3ea-bcc5b2b34e4d · outbound

This paper cites Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-12T12:51:40.805720Z

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Observation eb9f7c1f-8bf9-47df-8c1e-89f89a0ca52c · outbound

This paper cites Benchmarking rep- resentation learning for natural world image collections.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Benchmarking rep- resentation learning for natural world image collections

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.246902Z

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Observation f0207735-902a-490f-ab7a-cc868fb83273 · outbound

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

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Dense contrastive learning for self-supervised visual pre-training

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.229865Z

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Observation 6e243435-ddb0-472b-a2cb-a911647556c8 · outbound

This paper cites Freesolo: Learning to segment objects without annotations.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Freesolo: Learning to segment objects without annotations

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.214787Z

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Observation c66e3d65-c375-4764-927f-1bdf137c1228 · outbound

This paper cites Yu, and Ishan Misra.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Yu, and Ishan Misra

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.197642Z

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Observation abca6ed6-fbce-4b0b-9159-1b63055aeb7b · outbound

This paper cites Masked feature predic- tion for self-supervised visual pre-training.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Masked feature predic- tion for self-supervised visual pre-training

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.181143Z

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Observation d5b5a2a1-3c66-44c5-89fc-59427b65a672 · outbound

This paper cites Croco: Self-supervised pre-training for 3d vision tasks by cross-view completion.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Croco: Self-supervised pre-training for 3d vision tasks by cross-view completion

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.163241Z

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Observation 7d6034bf-36e8-4dff-a56e-d795fe740096 · outbound

This paper cites CroCo v2: Improved Cross-view Completion Pre- training for Stereo Matching and Optical Flow.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning CroCo v2: Improved Cross-view Completion Pre- training for Stereo Matching and Optical Flow

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T12:51:40.837072Z

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Observation 58113a81-9d98-4603-b3b7-e43aa5fe2227 · outbound

This paper cites CroCo: Self- Supervised Pre-training for 3D Vision Tasks by Cross-View Completion.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning CroCo: Self- Supervised Pre-training for 3D Vision Tasks by Cross-View Completion

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.141801Z

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Observation 25c53422-611c-42a6-be5c-ec628763c9a4 · outbound

This paper cites Un- supervised feature learning via non-parametric instance dis- crimination.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Un- supervised feature learning via non-parametric instance dis- crimination

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.127285Z

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Observation c3cce160-e538-4536-ad66-7381edb3666e · outbound

This paper cites Clusterfit: Improving general- ization of visual representations, 2019.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Clusterfit: Improving general- ization of visual representations, 2019

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.107096Z

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Observation bed80d65-8287-4c1a-bf0f-50591e9eb1ff · outbound

This paper cites Zamir, Alexander Sax, William B.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Zamir, Alexander Sax, William B

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.091210Z

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Observation 233e70bb-0b23-45f6-92ae-d439043583ac · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction, 2021.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Barlow twins: Self-supervised learning via redundancy reduction, 2021

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.074772Z

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Observation 0aaddf92-438d-496a-b20e-9a291338c405 · outbound

This paper cites Colorful image colorization.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Colorful image colorization

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.059806Z

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Observation 7c5892a1-d4c2-435d-84c6-dbdf0bb478ef · outbound

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

Probing the Mid-level Vision Capabilities of Self-Supervised Learning iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T12:51:40.863287Z

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Observation 6b8170c1-dcbb-41d0-af0a-70c4371003d9 · outbound

This paper cites The 2D projection error is then defined as: Error2D = ∥p′ − q∥2 where ∥ · ∥2 represents the Euclidean distance in the image plane.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning The 2D projection error is then defined as: Error2D = ∥p′ − q∥2 where ∥ · ∥2 represents the Euclidean distance in the image plane

Reference 74

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T12:51:41.032313Z

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Observation 89d9b158-c280-41fa-b8ab-9d70d6eaa657 · outbound

This paper cites an unresolved cited work.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning Unresolved cited work

Reference 2016

Resolution: parse uncertain; raw_fallback, observed 2026-08-12T12:51:41.405191Z

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Observation ea1ecf5e-ce67-4832-b795-d08d0e7ee2a3 · outbound

This paper cites student” network and a “target.

Probing the Mid-level Vision Capabilities of Self-Supervised Learning student” network and a “target

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:51:41.045901Z

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