Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-18T21:59:54.331461Z
Paper Citation Record · LEDGER
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report
As of 7 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2508.13401.
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 2026-08-07T06:34:17.273281+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
58 of 58 outbound references displayed
- verified exact3
- verified fuzzy53
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1923a560-b352-4e4b-b486-19341fc9f43f · outbound
This paper cites Arun Kumar and K.V .S.R.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Arun Kumar and K.V .S.R
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.865678Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8cb4eb89-296c-4f87-a7e0-7a1c1afd3cf0 · outbound
This paper cites Brander, Dale Dominey-Howes, C.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Brander, Dale Dominey-Howes, C
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.757285Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e7efa12a-7ddf-4cad-9c68-859f655dd595 · outbound
This paper cites Brander and A.D.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Brander and A.D
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.834184Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 98a1510b-4bb6-4634-afe6-b317fb594241 · outbound
This paper cites Chris Brewster, Richard E.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Chris Brewster, Richard E
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.775339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a046ba29-0a01-423f-9fef-dadd44c5662f · outbound
This paper cites Castelle, Tim Scott, R.W.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Castelle, Tim Scott, R.W
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.836854Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 38238fb5-2732-49da-8f9b-97a9b96c7cb3 · outbound
This paper cites Sparse instance activation for real-time instance segmentation.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Sparse instance activation for real-time instance segmentation
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.732618Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d187c70e-7a71-47bc-ae44-0597451f9f87 · outbound
This paper cites Explainable Rip Current Detection and Visualization with XAI EigenCAM.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Explainable Rip Current Detection and Visualization with XAI EigenCAM
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.735997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c7fd5024-2057-46b2-95bd-6951b368f705 · outbound
This paper cites AIM 2025 high FPS non-uniform motion deblurring challenge report.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report AIM 2025 high FPS non-uniform motion deblurring challenge report
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.845360Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2aa2b0d1-2946-4ea2-8b93-cd0ca01b1103 · outbound
This paper cites The cityscapes dataset for semantic urban scene understanding.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report The cityscapes dataset for semantic urban scene understanding
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.714938Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 65f94e50-758b-4c0b-a601-3de9e702086a · outbound
This paper cites an unresolved cited work.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-05-18T22:01:52.708359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ac31c958-afdb-46a0-b2a6-d384b9563ee1 · outbound
This paper cites Automated rip current detection with region based convolutional neural networks.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Automated rip current detection with region based convolutional neural networks
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.768696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 323d896c-5052-4bbd-ae7d-474649cd0ded · outbound
This paper cites Automated rip current detection with region based convolutional neural networks.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Automated rip current detection with region based convolutional neural networks
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.724980Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9ea0df96-da7c-492f-bf6b-782e8129ed70 · outbound
This paper cites RipViz: Find- ing Rip Currents by Learning Pathline Behavior.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report RipViz: Find- ing Rip Currents by Learning Pathline Behavior
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.842414Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5fdf2f93-8f65-412b-b965-6bf869a38c1b · outbound
This paper cites Rip currents in the non-tidal surf zone with sandbars: numerical analysis versus field measurements.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Rip currents in the non-tidal surf zone with sandbars: numerical analysis versus field measurements
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.871871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9efbbe27-c9cf-4e6c-a722-3a8dbf2bf0ff · outbound
This paper cites Rip Current Segmentation: A novel benchmark and YOLOv8 baseline results.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Rip Current Segmentation: A novel benchmark and YOLOv8 baseline results
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.747005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c8e363ca-d355-4536-9a66-81dc1b9dd5a3 · outbound
This paper cites AIM 2025 challenge on rip current seg- mentation (RipSeg).
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report AIM 2025 challenge on rip current seg- mentation (RipSeg)
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.750471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c316d8c3-812f-4f25-afec-d6ea95805716 · outbound
This paper cites Ripvis: Rip currents video instance segmentation benchmark for beach monitor- ing and safety.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Ripvis: Rip currents video instance segmentation benchmark for beach monitor- ing and safety
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.753822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ec04764d-ed8a-4842-a827-3b275361ef56 · outbound
This paper cites Efficient real-world deblurring using single images: AIM 2025 chal- lenge report.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Efficient real-world deblurring using single images: AIM 2025 chal- lenge report
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.764675Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4ff52dc7-ef50-452d-89a9-4f456275feab · outbound
This paper cites Cubuk, Quoc V.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Cubuk, Quoc V
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.798077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e6016c26-4add-43cb-880f-f6b68eb92da6 · outbound
This paper cites Mask R-CNN.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Mask R-CNN
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.711779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c71a559d-a135-4d3d-87bf-af71e5794439 · outbound
This paper cites Numerical study of rip currents inter- laced with multichannel sandbars.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Numerical study of rip currents inter- laced with multichannel sandbars
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.729040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6cbf49be-68b2-4a9e-84ee-9d024025d7c0 · outbound
This paper cites Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.739278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5b1b7d5b-15d8-41b2-a5d4-68bdb3cc1300 · outbound
This paper cites 4K image super-resolution on mobile NPUs: Mobile AI & AIM 2025 challenge report.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report 4K image super-resolution on mobile NPUs: Mobile AI & AIM 2025 challenge report
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.778881Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 77454156-d8a6-4ac0-b3b7-3b33e0d62f86 · outbound
This paper cites Efficient learned smartphone ISP on mobile GPUs: Mo- bile AI & AIM 2025 challenge report.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Efficient learned smartphone ISP on mobile GPUs: Mo- bile AI & AIM 2025 challenge report
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.718809Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6939205e-c3bb-4ffb-bcfa-eb2c6e33de31 · outbound
This paper cites Efficient image denoising on smartphone GPUs: Mobile AI & AIM 2025 challenge report.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Efficient image denoising on smartphone GPUs: Mobile AI & AIM 2025 challenge report
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.785533Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 205774e8-6333-4bdf-ba40-eab9133820cd · outbound
This paper cites Adapting stable diffusion for on-device inference: Mobile AI & AIM 2025 challenge report.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Adapting stable diffusion for on-device inference: Mobile AI & AIM 2025 challenge report
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.875365Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 937edac3-8d01-4bd4-8ab9-37cecdbc7b8a · outbound
This paper cites YOLO by Ultralytics.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report YOLO by Ultralytics
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.853346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 479f564c-78af-4206-91d6-725d0049ae17 · outbound
This paper cites AIM 2025 challenge on robust offline video super-resolution: Dataset, methods and results.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report AIM 2025 challenge on robust offline video super-resolution: Dataset, methods and results
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.722011Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1091ab55-6ace-4e05-b408-d2409a8d473a · outbound
This paper cites RipFinder: Real-time rip current detection on mobile devices.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report RipFinder: Real-time rip current detection on mobile devices
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.856624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 816d58b5-c142-4add-a478-8835bc6655d4 · outbound
This paper cites Rip- scout: Realtime ml-assisted rip current detection and auto- mated data collection using uavs.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Rip- scout: Realtime ml-assisted rip current detection and auto- mated data collection using uavs
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.859732Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6ffa379b-84ce-4900-aa9b-d12b8c737e97 · outbound
This paper cites YOLOv11: An Overview of the Key Architectural Enhancements.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report YOLOv11: An Overview of the Key Architectural Enhancements
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T22:01:51.950353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c824b550-6d33-431d-9166-d48081ac22a9 · outbound
This paper cites Segment anything.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Segment anything
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.862760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c6123a0f-2bdd-48e8-9db7-992ad62b50c0 · outbound
This paper cites Real-world raw de- noising using diverse cameras: AIM 2025 challenge report.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Real-world raw de- noising using diverse cameras: AIM 2025 challenge report
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.828431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f98a58f4-d36f-414c-b991-fa165b09abb2 · outbound
This paper cites Microsoft COCO: Common objects in context.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Microsoft COCO: Common objects in context
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.822224Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e7406d8c-6571-49bd-aa97-e26bb9a78f4f · outbound
This paper cites AIM 2025 perceptual image super-resolution chal- lenge.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report AIM 2025 perceptual image super-resolution chal- lenge
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.831469Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a6cff6b3-566b-405b-958e-0ffec76cfa50 · outbound
This paper cites an unresolved cited work.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Unresolved cited work
Reference 36
- Resolution
- unresolved
- raw_fallback, observed 2026-05-18T22:01:52.839691Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c5e3139b-f921-4c66-9ce4-1a289e0acec7 · outbound
This paper cites RTMDet: An Empirical Study of Designing Real-Time Object Detectors.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report RTMDet: An Empirical Study of Designing Real-Time Object Detectors
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T22:01:51.940994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5003ed12-0a78-435f-8586-89d960dc6b70 · outbound
This paper cites Machine learning appli- cations in detecting rip channels from images.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Machine learning appli- cations in detecting rip channels from images
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.813127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 185900c6-7998-4330-80de-1615227c10df · outbound
This paper cites McGill and Jean T.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report McGill and Jean T
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.819204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation cbfbe161-d509-4c88-a584-41e4a3366516 · outbound
This paper cites Flow-based rip current detection and visualiza- tion.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Flow-based rip current detection and visualiza- tion
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.810208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 26de2f51-319b-4cc3-9e2f-84fa3e3c3db3 · outbound
This paper cites What is a rip current? https://oceanservice.noaa.gov/facts/ripcurrent.html.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report What is a rip current? https://oceanservice.noaa.gov/facts/ripcurrent.html
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.816412Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6e16509c-ab11-43d0-8415-ea6a72ad3f5f · outbound
This paper cites Detecting and Visualizing Rip Current Using Optical Flow.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Detecting and Visualizing Rip Current Using Optical Flow
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.848079Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3dd94da6-5764-4fcd-9f73-f7f385e13d20 · outbound
This paper cites RipGAN: A GAN-based rip current data augmenta- tion method.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report RipGAN: A GAN-based rip current data augmenta- tion method
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.868636Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 74fffb2f-8b6a-4bee-be4b-985eabe16e8e · outbound
This paper cites Interpretable deep learning applied to rip cur- rent detection and localization.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Interpretable deep learning applied to rip cur- rent detection and localization
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.801110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2d10b1c1-b4a0-4015-b40b-e1b1bee1b491 · outbound
This paper cites Ripnet: A lightweight one-class deep neural network for the identification of rip currents.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Ripnet: A lightweight one-class deep neural network for the identification of rip currents
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.788816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a0c4053b-c4aa-4f0c-addc-b258d858ef86 · outbound
This paper cites RipDet: A fast and lightweight deep neural network for rip currents detection.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report RipDet: A fast and lightweight deep neural network for rip currents detection
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.792366Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ee1e0ca2-153f-4559-b772-6382d0456f2c · outbound
This paper cites Tanveer, and Michael Hobbs.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Tanveer, and Michael Hobbs
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.795189Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 626a2a8a-6a17-4ffd-94ce-ac0b8d4deaca · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report SAM 2: Segment Anything in Images and Videos
Reference 48
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T22:01:51.945766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 83fdd2c5-8507-49fa-a90b-782016bd5910 · outbound
This paper cites AIM 2025 challenge on screen-content video quality assessment: Methods and results.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report AIM 2025 challenge on screen-content video quality assessment: Methods and results
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.804201Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c642d590-f243-4f00-ac63-bb8ec127ea70 · outbound
This paper cites A review on YOLOv8 and its advancements.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report A review on YOLOv8 and its advancements
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.807220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c3e00a8f-bf4b-4961-a1ec-1b317226cd28 · outbound
This paper cites AIM 2025 challenge on inverse tone mapping report: Methods and results.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report AIM 2025 challenge on inverse tone mapping report: Methods and results
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.878053Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8abc3fcb-5bfa-495b-9106-59e08dadadd3 · outbound
This paper cites CBAM: Convolutional Block Attention Module.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report CBAM: Convolutional Block Attention Module
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.772357Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 824a9368-2610-4ba3-9da1-7b25019d61a9 · outbound
This paper cites Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.760794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation cad43f14-708e-49b1-b1dc-0c2f2d816e7c · outbound
This paper cites AIM 2025 low-light raw video denoising challenge: Dataset, methods and results.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report AIM 2025 low-light raw video denoising challenge: Dataset, methods and results
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.850814Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5c2f00e1-24b9-4218-8dda-d245b2ddc3a5 · outbound
This paper cites Video instance seg- mentation.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Video instance seg- mentation
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.881036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 76ad2471-a4a3-403e-bf2b-94a06b3650a4 · outbound
This paper cites The 3rd large-scale video object segmentation challenge - video in- stance segmentation track.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report The 3rd large-scale video object segmentation challenge - video in- stance segmentation track
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.825117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4f14c60c-0e57-47af-b05b-f989b96c78f7 · outbound
This paper cites Style-hallucinated dual consistency learn- ing for domain generalized semantic segmentation.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report Style-hallucinated dual consistency learn- ing for domain generalized semantic segmentation
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.782425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 44246376-ebce-4118-aaba-38d6541b0ad2 · outbound
This paper cites YOLO-Rip: A modified lightweight net- work for Rip currents detection.Frontiers in Marine Science, 9:930478.
AIM 2025 Rip Current Segmentation (RipSeg) Challenge Report YOLO-Rip: A modified lightweight net- work for Rip currents detection.Frontiers in Marine Science, 9:930478
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T22:01:52.743274Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.