MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning | Paper Citation Record

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This paper cites Learning to see by moving.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Learning to see by moving

Reference 1

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Observation db4cc989-9d1e-4b3f-9ce7-58e9ba9648bd · outbound

This paper cites Dense unsupervised learning for video segmentation.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Dense unsupervised learning for video segmentation

Reference 2

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Observation 13c712f3-8021-44a6-8684-a7ca781683c7 · outbound

This paper cites Self-labelling via simultaneous clustering and repre- sentation learning.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Self-labelling via simultaneous clustering and repre- sentation learning

Reference 3

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Observation 5fa130ba-ff4b-4ec3-85d2-d5da205a0a1e · outbound

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

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Self-supervised learning from images with a joint-embedding predictive architecture

Reference 4

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This paper cites Towards in-context scene understanding.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Towards in-context scene understanding

Reference 5

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Observation 1d675641-6f7d-4735-975b-3f1026a1f410 · outbound

This paper cites Object discovery from motion- guided tokens.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Object discovery from motion- guided tokens

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.911638Z

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Observation d7367ea5-5cdf-469b-9da0-cfb26d39e72d · outbound

This paper cites Revisiting feature prediction for learning visual representations from video.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Revisiting feature prediction for learning visual representations from video

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.897574Z

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Observation 27e3189d-ea2a-40a2-ac78-8ffa183f88cf · outbound

This paper cites Coco- stuff: Thing and stuff classes in context.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Coco- stuff: Thing and stuff classes in context

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.882640Z

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Observation 6bb67962-346e-422d-8ba9-08e228204557 · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.NeurIPS, 33:9912–9924, 2020.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unsupervised learning of visual features by contrasting cluster assignments.NeurIPS, 33:9912–9924, 2020

Reference 9

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Observation bd9fa9c9-c936-4917-8a25-829f13db9323 · outbound

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

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Emerg- ing properties in self-supervised vision transformers

Reference 10

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Observation 6cadd0b9-0116-42a5-a924-64ae95092733 · outbound

This paper cites Scaling 4D Representations.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Scaling 4D Representations

Reference 11

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Observation 17c52c79-fbe9-4d4f-a47d-c08096a1b860 · outbound

This paper cites Learning from one continuous video stream.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Learning from one continuous video stream

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.821616Z

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Observation 182bfc68-da98-4d00-a92e-cccd108142c4 · outbound

This paper cites Learning to Estimate Pose by Watching Videos.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Learning to Estimate Pose by Watching Videos

Reference 13

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Observation 18665462-73c9-4b25-a70b-9114599154f4 · outbound

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

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning A simple framework for contrastive learning of visual representations

Reference 14

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Observation e4968f67-54c4-4ff3-8cd5-49f9246e4267 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Sinkhorn distances: Lightspeed computation of optimal transport

Reference 15

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Observation c8baf89f-31ea-4a73-885c-fffa72619fc8 · outbound

This paper cites Vision transformers need registers.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Vision transformers need registers

Reference 16

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Observation fe81c31a-30a0-4439-99e6-5e0372cc285d · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.763155Z

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Observation b0b26563-c672-4d4d-9082-8d1708d63281 · outbound

This paper cites Everingham, L.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Everingham, L

Reference 18

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Observation f38531b7-bc40-47a9-a12c-2de4514047d6 · outbound

This paper cites Watching the World Go By: Representation Learning from Unlabeled Videos.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Watching the World Go By: Representation Learning from Unlabeled Videos

Reference 19

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Observation ad1bffd2-bc59-4783-bff7-eb3dc005edb8 · outbound

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

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Bootstrap your own latent-a new approach to self-supervised learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.735454Z

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Observation a4b75d5e-bc46-46e6-be33-e51189ca23f9 · outbound

This paper cites Accelerating large- scale inference with anisotropic vector quantization.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Accelerating large- scale inference with anisotropic vector quantization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.720995Z

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Observation a53d7706-bbb8-465c-ac96-063c5214b8e6 · outbound

This paper cites Stego: Unsupervised se- mantic segmentation by distilling feature correspondences.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Stego: Unsupervised se- mantic segmentation by distilling feature correspondences

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.706719Z

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Observation 4c850888-06f7-404e-b62a-ffc452de6766 · outbound

This paper cites Masked autoencoders are scalable vision learners.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Masked autoencoders are scalable vision learners

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.693105Z

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Observation c2b85fad-14f8-4cda-8c4b-86327cb6f434 · outbound

This paper cites Effi- cient visual pretraining with contrastive detection.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Effi- cient visual pretraining with contrastive detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.679458Z

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Observation a5c58e92-00c2-48f8-9742-110a5eee2ff2 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Gaussian Error Linear Units (GELUs)

Reference 25

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Observation ca019319-7844-459f-8274-314d9d032ae8 · outbound

This paper cites Learning image representations tied to ego-motion.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Learning image representations tied to ego-motion

Reference 26

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Observation f511e82b-8247-40e2-9e46-300a8da736e2 · outbound

This paper cites Invariant information clustering for unsupervised image classification and segmentation.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Invariant information clustering for unsupervised image classification and segmentation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.651367Z

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Observation a7711d1b-bfe8-4c1f-adb6-05a36ae1dd27 · outbound

This paper cites Billion-scale similarity search with gpus.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Billion-scale similarity search with gpus

Reference 28

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Observation e59e5412-cf0c-4f2b-98a6-2bbef57d5354 · outbound

This paper cites CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos

Reference 29

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Observation d7516c09-0e53-41d9-a207-54cbe2550047 · outbound

This paper cites Adam: A method for stochastic optimization.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Adam: A method for stochastic optimization

Reference 30

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Observation 024ba598-71be-44b2-9acf-026854c0f055 · outbound

This paper cites Panoptic segmentation.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Panoptic segmentation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.606974Z

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Observation 80e8bb61-0d9a-4f2b-8e0e-d8bb0a6c9be8 · outbound

This paper cites Principles of Gestalt psychology.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Principles of Gestalt psychology

Reference 32

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Observation bcbbafc5-6eba-4a87-87fc-48fee3b381f2 · outbound

This paper cites The hungarian method for the assignment problem.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning The hungarian method for the assignment problem

Reference 33

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Observation a473c0fd-6825-48e6-873a-627d4f313e00 · outbound

This paper cites Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:11:16.873751Z

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Observation eba137e3-08b7-4609-9903-f14dcb06d56e · outbound

This paper cites Smooseg: smoothness prior for unsupervised semantic segmentation.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Smooseg: smoothness prior for unsupervised semantic segmentation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.564248Z

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Observation d3ba9632-e338-4eb8-9358-d6bb24c3e14b · outbound

This paper cites Cribo: Self-supervised learning via cross-image object-level bootstrapping.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Cribo: Self-supervised learning via cross-image object-level bootstrapping

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.550456Z

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Observation a3546f6f-fe97-4ece-ad83-fa57bc57fe95 · outbound

This paper cites Joint-task self-supervised learning for temporal correspondence.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Joint-task self-supervised learning for temporal correspondence

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.535958Z

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Observation 8f6aae2f-212e-48fc-856d-e06f2c018bab · outbound

This paper cites Exploring plain vision transformer backbones for object de- tection.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Exploring plain vision transformer backbones for object de- tection

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.520544Z

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Observation 20d892ed-61fd-41a7-abed-1acf18153e02 · outbound

This paper cites Cross pixel optical-flow similarity for self-supervised learn- ing.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Cross pixel optical-flow similarity for self-supervised learn- ing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.506619Z

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Observation ca90867e-2199-49ba-97b0-76f5acbcaa51 · outbound

This paper cites Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and local- ization.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Deep spectral methods: A surprisingly strong baseline for unsupervised semantic segmentation and local- ization

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.492663Z

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Observation 06307d59-bc5f-47e5-bb58-1fb7be2b470a · outbound

This paper cites You don’t need domain- specific data augmentations when scaling self-supervised learning.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning You don’t need domain- specific data augmentations when scaling self-supervised learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.479018Z

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Observation 40c384e9-c58a-4f27-8fa2-c8fadf61cc07 · outbound

This paper cites Dinov2: Learning robust visual features without supervision.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Dinov2: Learning robust visual features without supervision

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.465064Z

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Observation 46c00e33-9e1b-4793-87bd-72e64ab2ec32 · outbound

This paper cites Hummingbird evaluation for vision encoders, 2024.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Hummingbird evaluation for vision encoders, 2024

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.434584Z

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Observation f08d6650-5ddb-4927-981d-e3bf9a6188b0 · outbound

This paper cites Burgh- outs, Francesco Locatello, and Yuki M Asano.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Burgh- outs, Francesco Locatello, and Yuki M Asano

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.419663Z

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Observation 06c25c65-b837-4448-8265-3baa32594d19 · outbound

This paper cites Self-supervised video pretraining yields human- aligned visual representations.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Self-supervised video pretraining yields human- aligned visual representations

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.404662Z

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Pytorch: An imperative style, high-performance deep learning library

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.390213Z

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Observation 018a3f0d-b30a-4bb2-a9b1-684f116bd56d · outbound

This paper cites Learning features by watching objects move.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Learning features by watching objects move

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.375299Z

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Observation 91bbeeca-c0cc-4a4f-8551-29af1a54bbe1 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning The 2017 DAVIS Challenge on Video Object Segmentation

Reference 48

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This paper cites Imagenet large scale visual recognition challenge.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Imagenet large scale visual recognition challenge

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.356844Z

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Observation 54768e16-f69b-4530-a2aa-7265f4dd5a78 · outbound

This paper cites Time does tell: Self-supervised time- tuning of dense image representations.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Time does tell: Self-supervised time- tuning of dense image representations

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.343071Z

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Observation 468c1c08-c59d-4f4b-b3ca-95faf364629a · outbound

This paper cites Sigma: Sinkhorn-guided masked video modeling.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Sigma: Sinkhorn-guided masked video modeling

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.328752Z

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Observation 58d97206-d89e-4e56-95fb-01237eec1668 · outbound

This paper cites Bridging the gap to real-world object-centric learning.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Bridging the gap to real-world object-centric learning

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.314533Z

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Observation 84a28121-699e-468a-a265-9a5eedb1135c · outbound

This paper cites Unsupervised object local- ization: Observing the background to discover objects.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unsupervised object local- ization: Observing the background to discover objects

Reference 53

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

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Croc: Cross-view on- line clustering for dense visual representation learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.281064Z

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This paper cites Segmenter: Transformer for semantic segmentation.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Segmenter: Transformer for semantic segmentation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.265682Z

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Observation bc506c39-e77a-4291-b75a-640141d90e7d · outbound

This paper cites EVA-CLIP: Improved Training Techniques for CLIP at Scale.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning EVA-CLIP: Improved Training Techniques for CLIP at Scale

Reference 56

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This paper cites Video- mae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Video- mae: Masked autoencoders are data-efficient learners for self-supervised video pre-training

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.250126Z

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This paper cites Self-supervised learning of video-induced visual invariances.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Self-supervised learning of video-induced visual invariances

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.234231Z

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This paper cites Unsupervised semantic segmenta- tion by contrasting object mask proposals.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unsupervised semantic segmenta- tion by contrasting object mask proposals

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.219664Z

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This paper cites Moving off-the- grid: Scene-grounded video representations.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Moving off-the- grid: Scene-grounded video representations

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.203721Z

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Observation 6f02235c-047f-462f-a6c3-2df10c888b85 · outbound

This paper cites Is imagenet worth 1 video? learning strong image encoders from 1 long unlabelled video.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Is imagenet worth 1 video? learning strong image encoders from 1 long unlabelled video

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.188029Z

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This paper cites Unsupervised learning of visual representations using videos.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unsupervised learning of visual representations using videos

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.172796Z

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Observation 57dbde63-7b0a-4e9c-b83f-030675d1f989 · outbound

This paper cites Learning correspondence from the cycle-consistency of time.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Learning correspondence from the cycle-consistency of time

Reference 63

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MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Self-supervised representation learning from flow equivariance

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.146856Z

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Observation cfdfe01c-8a54-4f66-95bb-e22002ecb1c1 · outbound

This paper cites YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Reference 65

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This paper cites Patch-level representation learning for self-supervised vision transformers.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Patch-level representation learning for self-supervised vision transformers

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.131030Z

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Observation 555cff07-47cf-4183-b3c5-5a08fde2fefb · outbound

This paper cites Unsupervised se- mantic segmentation with self-supervised object-centric rep- resentations.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unsupervised se- mantic segmentation with self-supervised object-centric rep- resentations

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.115443Z

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Observation 89c4e8e3-3614-49ee-bc27-bd963e8ac1d1 · outbound

This paper cites Object-centric learning for real-world videos by predicting temporal feature similarities.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Object-centric learning for real-world videos by predicting temporal feature similarities

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.100881Z

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Observation cb1c8b9f-500d-4f0b-b08a-4fcfd1972e4c · outbound

This paper cites Scene parsing through ade20k dataset.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Scene parsing through ade20k dataset

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.085705Z

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Observation c96bf7d4-82c5-41c0-b6ec-0d19469efa9b · outbound

This paper cites ibot: Image bert pre-training with online tokenizer.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning ibot: Image bert pre-training with online tokenizer

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.071068Z

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Observation 58139f79-37c8-49cb-973a-4859850ba563 · outbound

This paper cites Self-supervised learning of object parts for semantic segmentation.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Self-supervised learning of object parts for semantic segmentation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:17.055445Z

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Observation 6687d7c3-3bce-428c-99d0-4d8e5fdcb97f · outbound

This paper cites Additional Experiments Comparison to TimeT.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Additional Experiments Comparison to TimeT

Reference 74

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Observation 76863aef-2a0d-4715-9779-3f6b1a9b92f2 · outbound

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MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:11:17.018983Z

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Observation ba5a31da-939c-4936-9678-2c4b9d6c8716 · outbound

This paper cites Unsupervised video semantic segmentation results for clustering and over-clustering on DA VIS [48] and Youtube-VOS (YTVOS) [65].

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unsupervised video semantic segmentation results for clustering and over-clustering on DA VIS [48] and Youtube-VOS (YTVOS) [65]

Reference 76

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T05:11:17.004018Z

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Observation 10906272-b95b-4d3c-a356-d43e6ba570e4 · outbound

This paper cites Since the original implementation by [5] is unavailable, we use the open-source implementation from [ 43].

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Since the original implementation by [5] is unavailable, we use the open-source implementation from [ 43]

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:16.988800Z

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Observation 1d7791e0-af2f-46fd-a9a6-27acd6a7d124 · outbound

This paper cites stuff" categories and 80.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning stuff" categories and 80

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:11:16.973690Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ee42fb40-d37d-4d2c-8c27-eaf6b39e8289 · outbound

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MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unresolved cited work

Reference 2021

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Observation 97000146-40f9-406a-a199-cb26e262ef5a · outbound

This paper cites an unresolved cited work.

MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning Unresolved cited work

Reference 2024

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Reference resolution

Outbound references

Pith citing papers