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Paper Citation Record · LEDGER

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning

As of 20 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2507.04317.

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

pith.paper-citation-record.v1
2507.04317 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:54:39.775138Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

  • verified exact8
  • verified fuzzy18
  • unresolved21
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2117ba29-dd76-46d0-82d0-6a17c09db356 · outbound

This paper cites The Rise of Minimally Invasive Surgery: 16 Year Analysis of the Progres- sive Replacement of Open Surgery with Laparoscopy.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning The Rise of Minimally Invasive Surgery: 16 Year Analysis of the Progres- sive Replacement of Open Surgery with Laparoscopy

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e840957b-2bbc-40dd-b3ee-f36ba8c2f330 · outbound

This paper cites Minimally Invasive Versus Open Lumbar Fusion: A Comparison of Blood Loss, Surgical Complications, and Hospital Course.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Minimally Invasive Versus Open Lumbar Fusion: A Comparison of Blood Loss, Surgical Complications, and Hospital Course

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 1e707feb-d3bc-4f31-ba03-45f5c30d3b5d · outbound

This paper cites Minimally invasive spine surgery decreases postoperative pain and inflammation for patients with lumbar spinal stenosis.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Minimally invasive spine surgery decreases postoperative pain and inflammation for patients with lumbar spinal stenosis

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation abf04d50-8f7c-44c1-9f9c-ee3633988201 · outbound

This paper cites Impact of minimally invasive surgery on immune function and stress response in gastric cancer patients,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Impact of minimally invasive surgery on immune function and stress response in gastric cancer patients,

Reference 4

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doi, observed 2026-08-06T19:54:40.521398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation cb75ee7a-963d-49b0-9713-9573bee20b8b · outbound

This paper cites Does Minimally Invasive Mitral Valve Repair Mean Less Postoperative Pain? The Annals of Thoracic Surgery.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Does Minimally Invasive Mitral Valve Repair Mean Less Postoperative Pain? The Annals of Thoracic Surgery

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-06T19:54:42.038038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation d9179218-0874-4549-82ec-eb3ce6fa54d7 · outbound

This paper cites Artificial intelligence- based computer vision in surgery: Recent advances and future perspectives.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Artificial intelligence- based computer vision in surgery: Recent advances and future perspectives

Reference 6

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verified exact
doi, observed 2026-08-06T19:54:40.050597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 25af26d1-712d-4713-a02f-d9949f90754c · outbound

This paper cites Digital tools and innovative healthcare solutions: Serious games and gamification in surgical training and patient care,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Digital tools and innovative healthcare solutions: Serious games and gamification in surgical training and patient care,

Reference 7

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raw_fallback, observed 2026-08-06T19:54:42.028193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 44b2a0fd-c657-46e0-a8f5-ec408c927e32 · outbound

This paper cites Computer Vi- sion in the Operating Room: Opportunities and Caveats,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Computer Vi- sion in the Operating Room: Opportunities and Caveats,

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0077143e-e4ba-4a2c-98a6-5254897f68c5 · outbound

This paper cites Novel applications of deep learning in surgical training,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Novel applications of deep learning in surgical training,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 3c2da3fb-b184-4fd9-b2f4-82a52808428e · outbound

This paper cites Segmentation of surgical instruments in laparoscopic videos: training dataset generation and deep-learning- based framework.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Segmentation of surgical instruments in laparoscopic videos: training dataset generation and deep-learning- based framework

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 390df135-3634-4202-92a0-9bc6a6caa469 · outbound

This paper cites Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 8f1658a4-c4dd-4fd5-aff4-6cee07a33272 · outbound

This paper cites Deep learning for surgical instrument recognition and segmentation in robotic- assisted surgeries: a systematic review,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Deep learning for surgical instrument recognition and segmentation in robotic- assisted surgeries: a systematic review,

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 35aea0e0-ff84-4499-a0b7-e2eca6c05464 · outbound

This paper cites S3Net: A Single Stream Structure for Depth Guided Image Relighting.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning S3Net: A Single Stream Structure for Depth Guided Image Relighting

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:37.554130Z digest=sha256:b0ed259f76cdc1bd415566fe7a232b22bb887562a0fde5bddc22a12780b42fe5

Observation 99a1ef7c-0936-4140-9bc3-db122fee9307 · outbound

This paper cites MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning MATIS: Masked-Attention Transformers for Surgical Instrument Segmentation,

Reference 14

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Unavailable: canonical work link unavailable.

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Observation 7bce4d50-ca52-4ea8-bba0-cd240d8adb59 · outbound

This paper cites U-Net: Convolutional Networks for Biomedical Image Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning U-Net: Convolutional Networks for Biomedical Image Segmentation

Reference 15

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Unavailable: canonical work link unavailable.

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Observation 90ea689a-faf3-4615-a24b-d5a35a4b2819 · outbound

This paper cites Segment Anything.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Segment Anything

Reference 16

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Observation 68a3a034-4946-4916-8007-372c479b0fe2 · outbound

This paper cites Track Anything: Segment Anything Meets Videos.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Track Anything: Segment Anything Meets Videos

Reference 17

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Observation 8c1a4ef5-9eec-4c0d-9f44-d0b04046bd4a · outbound

This paper cites Personalize Segment Anything Model with One Shot.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Personalize Segment Anything Model with One Shot

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 47166685-3f7f-4e81-b84a-36d59a0de644 · outbound

This paper cites SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8b2d5209-9f4b-4426-a4b5-b39643efa823 · outbound

This paper cites DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:38.119846Z digest=sha256:b8b3f9da8f29847d643d95bb3d5c3511239864aca4d6faa5be3b3f60b156b7ca

Observation 6ca32da9-a389-4dc0-ada0-f386306c3a1c · outbound

This paper cites Segmentation of Brain Tumor in MRI Scans Using Deep Learning,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Segmentation of Brain Tumor in MRI Scans Using Deep Learning,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation dfad9add-cf04-4e0a-af08-ce02c1967b2a · outbound

This paper cites Object Segmentation for Robotic Grasping in Cluttered Environments,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Object Segmentation for Robotic Grasping in Cluttered Environments,

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 7ffc26f5-106a-482c-b3ff-ab7ede30b769 · outbound

This paper cites an unresolved cited work.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation adef43a9-599e-418d-ab4c-3df53f4e30c7 · outbound

This paper cites U-Net: Convolutional Net- works for Biomedical Image Segmentation,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning U-Net: Convolutional Net- works for Biomedical Image Segmentation,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:54:41.979282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation cd236959-7812-4941-a8a6-645aea318a2f · outbound

This paper cites Dermatologist-level Classification of Skin Cancer with Deep Neural Networks,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Dermatologist-level Classification of Skin Cancer with Deep Neural Networks,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 75542f48-5d49-4c9e-8448-a8a09cd935a5 · outbound

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

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 0249064e-633a-4538-b228-f5458cc3ea1f · outbound

This paper cites UNETR: Transformers for 3D Medical Image Segmentation,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning UNETR: Transformers for 3D Medical Image Segmentation,

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-06T19:54:41.949426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5b6fb987-135f-4aae-8a98-215a47502110 · outbound

This paper cites Segmenter: Transformer for Semantic Segmentation,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Segmenter: Transformer for Semantic Segmentation,

Reference 28

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raw_fallback, observed 2026-08-06T19:54:41.940035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 9aabc543-ad8c-4e53-ac0d-295c36843cf0 · outbound

This paper cites SETR: Vision Transformers for Semantic Segmenta- tion,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning SETR: Vision Transformers for Semantic Segmenta- tion,

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-06T19:54:41.931108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:38.711349Z digest=sha256:fe2414b50044277a77718e99bf8af77de9246b9c1e0a923a79b869f00fb0a074

Observation 7987409b-511c-49b8-aecc-825abd67b3e6 · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 30

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e28d11a5-1c1c-47ef-a1c7-cc8832845087 · outbound

This paper cites Prostate Segmentation Using Hybrid Transformer-CNN Models,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Prostate Segmentation Using Hybrid Transformer-CNN Models,

Reference 31

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raw_fallback, observed 2026-08-06T19:54:41.921510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a22052e9-ca01-4c1c-ac99-554585ccbf2a · outbound

This paper cites SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers,

Reference 32

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raw_fallback, observed 2026-08-06T19:54:41.911912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:38.933296Z digest=sha256:0861ae178ce6221ce9c1e220a44a982507a9cc2736ad17743e5c58fbc3010b7c

Observation a3ea7f50-0ca6-4116-ad0c-b6be99fe7bcb · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Learning Transferable Visual Models From Natural Language Supervision,

Reference 33

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raw_fallback, observed 2026-08-06T19:54:41.902071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:38.973501Z digest=sha256:87f391910156327ed11b665541b683a6459b8682c9ead7b5bce119c1a1d3fc01

Observation d2e2828d-c010-48b9-8b01-ecc11a1d32ab · outbound

This paper cites Zero-Shot Medical Image Segmentation Using CLIP,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Zero-Shot Medical Image Segmentation Using CLIP,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:54:41.892960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:39.055174Z digest=sha256:7f927c1a832aadcdbdad9928f4b7693399ba4d69fd96fb6b5e344543e6b54516

Observation 82005ae4-b23d-42ac-82a1-731b1d9e7fdf · outbound

This paper cites BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T19:54:39.134498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:39.134498Z digest=sha256:d66401597e184a89917a0a9c0aa229430378b74288754fbdab90cde792fbdda4

Observation e729320c-5e81-46c6-826b-12aa82db4804 · outbound

This paper cites Vision-Language Models for Surgical Assistance: A Case Study in Laparoscopic Surgery,.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Vision-Language Models for Surgical Assistance: A Case Study in Laparoscopic Surgery,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:54:41.883869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:39.187275Z digest=sha256:6f4b0b4e14734aad119dd44ad8006304cbddec03ff8d6e5fa637abe370842efe

Observation e7b64698-c1fa-4a9a-90a6-d5d75302b32b · outbound

This paper cites A Simple Framework for Contrastive Learning of Visual Representations.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning A Simple Framework for Contrastive Learning of Visual Representations

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T19:54:39.253513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:39.253513Z digest=sha256:53c386d7d05593398a50ec265eeefc0a9e8dddd91117fd41ac535be77ae73e78

Observation ff1cb08c-ef96-4deb-93f3-b108fd2d6c7c · outbound

This paper cites an unresolved cited work.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 38

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:54:41.873504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:39.290513Z digest=sha256:3f5f9011f837296153646d1984ff55e44d2296dcd778dcb66c6a9ea9c7c1098e

Observation 33b2d0cc-3a45-4735-bdfa-7134e1ab2c12 · outbound

This paper cites Bengio, J.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Bengio, J

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:54:41.864639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:39.293640Z digest=sha256:b6fc45967821807044493caf2860ce87e78d4bbbdf57d5381fafb3557739b8ec

Observation 2401e4fa-5dd6-4921-9085-1544e41ea93f · outbound

This paper cites an unresolved cited work.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:54:41.855599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:39.296444Z digest=sha256:f8a5050791647e59bbe3d921be37cd6bec29eeaff8902210f112dbe4d8b41ebe

Observation 5d322b65-fc5c-4360-a88b-59fa85212615 · outbound

This paper cites an unresolved cited work.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 41

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:54:41.846544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:39.299542Z digest=sha256:76788ab08ea8e59479e182c1c24d507f15b2b4d144672ce79d2b565bf9bb128c

Observation 948c36f6-0bf7-4b09-a206-9aaf7fa157f3 · outbound

This paper cites an unresolved cited work.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Unresolved cited work

Reference 42

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:54:41.838084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:39.371567Z digest=sha256:9247c9b8695684a897657f1fed6c0d27da4a2494f6788b931ca974705b716501

Observation 5510acdb-cf12-4d28-9857-db19e3aeb287 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T19:54:39.576293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:39.576293Z digest=sha256:72141c2850389937a8b5e6e1bd4464d022f5fbcfdb933aecbeb066d7a315178b

Observation 2d750b7f-142f-4b80-ab8d-c9fcc45cab0f · outbound

This paper cites 2017 Robotic Instrument Segmentation Challenge.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning 2017 Robotic Instrument Segmentation Challenge

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T19:54:39.650058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:39.650058Z digest=sha256:92b3c22d85e56325d056110df77fc3832fddb6f9774df428290ead1883012836

Observation 7c870053-8de5-4233-98ce-0a35e8877fa6 · outbound

This paper cites 2018 Robotic Scene Segmentation Challenge.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning 2018 Robotic Scene Segmentation Challenge

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T19:54:39.708182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:39.708182Z digest=sha256:53b6ae54d1456ae117af5995c1e0845dbaef419c113a6264ecfc79a9e52352b4

Observation 57f94bbd-4bde-4189-9a01-818d4e014975 · outbound

This paper cites ISINet: An Instance-Based Approach for Surgical Instrument Segmentation.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning ISINet: An Instance-Based Approach for Surgical Instrument Segmentation

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T19:54:39.775138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:54:39.775138Z digest=sha256:b8e80f4e796156b11d6ab46441f5325b1bb7c506438780a1e1b103cc76ae8df2

Observation 1e1e431b-1c3e-4fd2-9a2c-83023199ec02 · outbound

This paper cites Available from: https://www.sciencedirect.com/science/article/pii/S0003497522014308.

CLIP-RL: Surgical Scene Segmentation Using Contrastive Language-Vision Pretraining & Reinforcement Learning Available from: https://www.sciencedirect.com/science/article/pii/S0003497522014308

Reference 1178

Resolution
verified exact
doi, observed 2026-08-06T19:54:40.323228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-06T19:54:36.837067Z digest=sha256:8f8b41c878059457253e0ab0c698b98162084885bd220a6305e48b979a1f39d7

Pith citing papers

No inbound Pith citation observations are available.