Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-13T16:50:57.376622Z
Paper Citation Record · LEDGER
Training a Student Expert via Semi-Supervised Foundation Model Distillation
As of 6 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2604.03841.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
64 of 64 outbound references displayed
- verified exact21
- verified fuzzy39
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch3
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 4e3c2354-d1c6-4bf2-b83a-b458f7aface6 · outbound
This paper cites Semi-supervised semantic segmenta- tion with pixel-level contrastive learning from a class-wise memory bank.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Semi-supervised semantic segmenta- tion with pixel-level contrastive learning from a class-wise memory bank
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.599376Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2953f05b-8579-4a29-a1aa-9770c3cbffd8 · outbound
This paper cites Foundation models defining a new era in vision: a survey and outlook.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Foundation models defining a new era in vision: a survey and outlook
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.618956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation f03ac35c-acc6-4293-a6c1-a30da6e6d957 · outbound
This paper cites Guided distillation for semi-supervised instance segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Guided distillation for semi-supervised instance segmentation
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.656015Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 03b9b4e6-ac72-4527-9965-85dae91f7f82 · outbound
This paper cites Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T20:45:15.979924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 0620c7f6-c056-42e2-bc19-5b4cc0fc1c41 · outbound
This paper cites On the Opportunities and Risks of Foundation Models.
Training a Student Expert via Semi-Supervised Foundation Model Distillation On the Opportunities and Risks of Foundation Models
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T16:53:00.064123Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 50e9d79e-09d9-4f38-8b9f-d63d1a9f7152 · outbound
This paper cites Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Curriculum labeling: Revisiting pseudo-labeling for semi-supervised learning
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.595342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation bd4b1c2d-8fb6-415e-b8a8-4971fb3f2c4f · outbound
This paper cites A simple framework for contrastive learning of visual representations.
Training a Student Expert via Semi-Supervised Foundation Model Distillation A simple framework for contrastive learning of visual representations
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.615268Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5238889a-b03a-4363-b518-c7b8dcd25ff6 · outbound
This paper cites Big self-supervised mod- els are strong semi-supervised learners.Advances in neural information processing systems, 33:22243–22255.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Big self-supervised mod- els are strong semi-supervised learners.Advances in neural information processing systems, 33:22243–22255
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.611906Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation ec098424-4157-4a91-a50f-9e91677d53cf · outbound
This paper cites An empirical study of training self-supervised vision transformers.
Training a Student Expert via Semi-Supervised Foundation Model Distillation An empirical study of training self-supervised vision transformers
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.652490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6b805e6c-eac3-490e-9587-b6ba218e14ae · outbound
This paper cites Semi-supervised semantic segmentation with cross pseudo supervision.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Semi-supervised semantic segmentation with cross pseudo supervision
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.659965Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation cd7d9143-0ccf-43cc-bc36-3e786cceab79 · outbound
This paper cites Depth-Guided Semi-Supervised Instance Segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Depth-Guided Semi-Supervised Instance Segmentation
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.074925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8f6362d1-2159-4b5e-9d07-f976f8460f5d · outbound
This paper cites Masked-attention mask trans- former for universal image segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Masked-attention mask trans- former for universal image segmentation
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.622765Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6906a224-9a59-4ded-aebc-16fbdd1a80e5 · outbound
This paper cites The cityscapes dataset for se- mantic urban scene understanding.
Training a Student Expert via Semi-Supervised Foundation Model Distillation The cityscapes dataset for se- mantic urban scene understanding
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.630510Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 1c50fd6a-395b-4520-86bc-c0dee93c111b · outbound
This paper cites Augmentation-free dense contrastive knowledge distilla- tion for efficient semantic segmentation.Advances in Neural Information Processing Systems, 36:51359–51370.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Augmentation-free dense contrastive knowledge distilla- tion for efficient semantic segmentation.Advances in Neural Information Processing Systems, 36:51359–51370
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.591296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b81ddde6-db32-4b51-b9cd-fcecf6d1e94a · outbound
This paper cites SEED: Self-supervised Distillation For Visual Representation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation SEED: Self-supervised Distillation For Visual Representation
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.069783Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation a8b2f518-89b8-404c-8038-034dddedf320 · outbound
This paper cites Foundation models in robotics: Applications, challenges, and the fu- ture.The International Journal of Robotics Research, page 02783649241281508.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Foundation models in robotics: Applications, challenges, and the fu- ture.The International Journal of Robotics Research, page 02783649241281508
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.603337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation a67a0301-b3d6-47b6-b4bb-4cfd195f76d7 · outbound
This paper cites Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.091010Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e9c4dd77-d2b6-4c24-86bd-f6a999f5c715 · outbound
This paper cites Mask r-cnn.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Mask r-cnn
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.637458Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5d9b9251-5058-4ddd-8032-7689eccb1d67 · outbound
This paper cites Distilling the Knowledge in a Neural Network.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Distilling the Knowledge in a Neural Network
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T16:53:00.093795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 1d51c3d6-c6ca-4d77-9f56-fdbbae755f09 · outbound
This paper cites Pseudo-label alignment for semi-supervised instance segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Pseudo-label alignment for semi-supervised instance segmentation
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.579378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6b62ca63-7f10-4eef-a734-6e5b5b4d6017 · outbound
This paper cites Pixel-wise contrastive distillation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Pixel-wise contrastive distillation
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.608149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 32ddff54-7993-49bf-bb1e-09dcf829fcd4 · outbound
This paper cites Vl2lite: Task-specific knowledge distillation from large vision- language models to lightweight networks.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Vl2lite: Task-specific knowledge distillation from large vision- language models to lightweight networks
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.645112Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e4455ff0-5b63-404f-b5aa-8c250e7b43f5 · outbound
This paper cites Mtkd: Multi-teacher knowledge distillation for image super- resolution.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Mtkd: Multi-teacher knowledge distillation for image super- resolution
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.587287Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3c5395dd-17da-42e9-8627-a3a377430871 · outbound
This paper cites Segment Anything.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Segment Anything
Reference 24
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-13T16:53:00.050812Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation a9267981-4d05-4ae3-bd9b-fe9259aa6313 · outbound
This paper cites Customkd: Customiz- ing large vision foundation for edge model improvement via knowledge distillation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Customkd: Customiz- ing large vision foundation for edge model improvement via knowledge distillation
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.641243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 989a5e34-a7d0-4f0f-80f8-7dcd584c0a11 · outbound
This paper cites Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Task-Specific Knowledge Distillation from the Vision Foundation Model for Enhanced Medical Image Segmentation
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.048562Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation e79f2ef8-7796-41af-a827-b7bbfb6683c6 · outbound
This paper cites Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.105296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b1543973-1eb1-474f-8058-537b7be97bd6 · outbound
This paper cites Grounding dino: Marrying dino with grounded pre-training for open-set object detection.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.648591Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 25f32ad7-05ec-476f-9346-44795dab17c2 · outbound
This paper cites Unbiased Teacher for Semi-Supervised Object Detection.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Unbiased Teacher for Semi-Supervised Object Detection
Reference 29
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T16:53:00.053435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 4a4a962f-6a81-46bf-987c-81fc02bbfe98 · outbound
This paper cites an unresolved cited work.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Unresolved cited work
Reference 30
- Resolution
- unresolved
- raw_fallback, observed 2026-05-14T05:53:14.633937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6e9a7c19-e236-41d7-92ab-6f41d3141e07 · outbound
This paper cites DINOv2: Learning Robust Visual Features without Supervision.
Training a Student Expert via Semi-Supervised Foundation Model Distillation DINOv2: Learning Robust Visual Features without Supervision
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T16:53:00.072377Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 52534aa5-f7ac-460a-b08d-1a188f3565a8 · outbound
This paper cites Self-supervised Knowledge Distillation for Few-shot Learning.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Self-supervised Knowledge Distillation for Few-shot Learning
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.056231Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 1713860f-fb44-4241-a865-32a649385086 · outbound
This paper cites Vi- sion transformers for dense prediction.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Vi- sion transformers for dense prediction
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.626522Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8d2a0612-451e-4c86-b6bb-9bfb1ebd7f13 · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
Training a Student Expert via Semi-Supervised Foundation Model Distillation SAM 2: Segment Anything in Images and Videos
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T16:53:00.099301Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation d24b02f6-7893-4ae0-96ef-78cb45e83a71 · outbound
This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T16:53:00.045846Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 62e097a4-1a66-4295-b4ee-0cae7a9736e6 · outbound
This paper cites Channel-wise knowledge distillation for dense prediction.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Channel-wise knowledge distillation for dense prediction
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.583632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 96fa4867-9652-4c67-8075-88314dffdf4f · outbound
This paper cites Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognition.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognition
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.080565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 344a70cd-620b-4bdb-a852-a0e172e4f05f · outbound
This paper cites Dime-fm: Distilling multi- modal and efficient foundation models.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Dime-fm: Distilling multi- modal and efficient foundation models
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.559426Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation a4aee906-12d1-469a-a50b-a2b79f43f939 · outbound
This paper cites Navidrivevlm: Decoupling high-level reasoning and motion planning for autonomous driving.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Navidrivevlm: Decoupling high-level reasoning and motion planning for autonomous driving
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.088322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b8815901-5771-48df-ba65-a37044257665 · outbound
This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.Advances in neural information processing systems, 30
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.527152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation bf78c76c-bf86-4998-be8c-925e6baf85ff · outbound
This paper cites Contrastive Representation Distillation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Contrastive Representation Distillation
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.067163Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3c498bb5-3ef8-4b5a-9564-04ece55b0a60 · outbound
This paper cites Knowledge transfer from vision foundation models for efficient training of small task-specific models.ICML2024.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Knowledge transfer from vision foundation models for efficient training of small task-specific models.ICML2024
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.539049Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3f67babe-de1b-4708-bb83-57820643fcc3 · outbound
This paper cites Sam-clip: Merging vision foundation models towards semantic and spatial understanding.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Sam-clip: Merging vision foundation models towards semantic and spatial understanding
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.563332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5be03a19-ea2b-4d0d-9671-19dfdf4989d7 · outbound
This paper cites Dense contrastive learning for self-supervised visual pre-training.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Dense contrastive learning for self-supervised visual pre-training
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.506596Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 9ece0739-deeb-45bd-8e07-8276b17c1f86 · outbound
This paper cites Contrastmask: Contrastive learning to segment every thing.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Contrastmask: Contrastive learning to segment every thing
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.531446Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 87c4cb0c-c526-44e8-b59d-49a1661d97e6 · outbound
This paper cites Detco: Unsuper- vised contrastive learning for object detection.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Detco: Unsuper- vised contrastive learning for object detection
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.555333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3d1bde9c-bd4e-4c56-9ac4-be1b82527ac4 · outbound
This paper cites Self-training with noisy student improves imagenet clas- sification.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Self-training with noisy student improves imagenet clas- sification
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.515523Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation efbdd2a0-48f4-43d4-bc39-d9c2025c7aca · outbound
This paper cites Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.535273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8a481545-a679-4299-bf6f-b4d3fa39505e · outbound
This paper cites A Survey on Knowledge Distillation of Large Language Models.
Training a Student Expert via Semi-Supervised Foundation Model Distillation A Survey on Knowledge Distillation of Large Language Models
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T23:31:12.027711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation de865685-4f6f-4c08-b4e6-97453d3968c5 · outbound
This paper cites Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
Reference 50
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T16:53:00.108192Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8346c8a0-5b8b-45d1-94ad-4fcb7d55a4e1 · outbound
This paper cites Cross-image relational knowl- edge distillation for semantic segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Cross-image relational knowl- edge distillation for semantic segmentation
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.547432Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation af2fa951-0c17-4a15-a144-085262748e18 · outbound
This paper cites Clip-kd: An empirical study of clip model distillation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Clip-kd: An empirical study of clip model distillation
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.510858Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 3fc9f8c7-e9cf-4573-a71b-93627e38e335 · outbound
This paper cites Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.096657Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2fe1901a-a322-4efe-a29c-fe09fd798f3b · outbound
This paper cites Revisiting weak-to-strong consistency in semi- supervised semantic segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Revisiting weak-to-strong consistency in semi- supervised semantic segmentation
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.543136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation b853cac6-eb4c-4407-9441-59a64be59df4 · outbound
This paper cites Depth Anything V2.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Depth Anything V2
Reference 55
- Resolution
- verified exact
- local_arxiv, observed 2026-05-13T16:53:00.085565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 85e68005-d9f9-4b76-9783-ab6563466bf5 · outbound
This paper cites G-detkd: Towards general distillation frame- work for object detectors via contrastive and semantic-guided feature imitation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation G-detkd: Towards general distillation frame- work for object detectors via contrastive and semantic-guided feature imitation
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.551578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation c497e23d-db16-493b-9689-4d959c7e5887 · outbound
This paper cites S^4M: Boosting Semi-Supervised Instance Segmentation with SAM.
Training a Student Expert via Semi-Supervised Foundation Model Distillation S^4M: Boosting Semi-Supervised Instance Segmentation with SAM
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.059048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 48e2fb82-c8e2-497b-a380-ad09c70eccc2 · outbound
This paper cites Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T11:39:22.805579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation f855aa6b-8a85-4c7a-a6f0-505e8b475343 · outbound
This paper cites Accessing vision foundation models via imagenet-1k.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Accessing vision foundation models via imagenet-1k
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.522727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 540a292e-b921-46dc-a206-39bcb5603324 · outbound
This paper cites An Open and Comprehensive Pipeline for Unified Object Grounding and Detection.
Training a Student Expert via Semi-Supervised Foundation Model Distillation An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T16:53:00.102253Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation adeda977-5933-4977-b791-ba0faa4c8580 · outbound
This paper cites Pixel contrastive-consistent semi-supervised semantic segmentation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Pixel contrastive-consistent semi-supervised semantic segmentation
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.575410Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 2a51168d-5243-4c68-8b2a-2a2f157488f1 · outbound
This paper cites Semantic under- standing of scenes through the ade20k dataset.International Journal of Computer Vision, 127:302–321.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Semantic under- standing of scenes through the ade20k dataset.International Journal of Computer Vision, 127:302–321
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.519089Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 7e4a2391-f326-4732-91f8-f8776fc997a9 · outbound
This paper cites Com- plementary relation contrastive distillation.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Com- plementary relation contrastive distillation
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.567378Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 6fc46e62-5706-45a4-860a-30a775bf82da · outbound
This paper cites Ar- gus: A compact and versatile foundation model for vision.
Training a Student Expert via Semi-Supervised Foundation Model Distillation Ar- gus: A compact and versatile foundation model for vision
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-14T05:53:14.571502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Pith citing papers
No inbound Pith citation observations are available.