Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-24T21:46:17.736097Z
Paper Citation Record · LEDGER
Understanding Deep Learning Techniques for Image Segmentation
As of 5 August 2026, this Paper Citation Record lists 100 of 221 outbound references and 0 inbound Pith citation observations for arXiv:1907.06119.
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 2026-08-05T06:32:48.257954+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
100 of 221 outbound references displayed
- verified exact14
- verified fuzzy76
- unresolved9
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 13f31fb9-94e9-4b59-b62b-ce13597feeb1 · outbound
This paper cites Slic superpixels compared to state-of-the-art superpixel methods.
Understanding Deep Learning Techniques for Image Segmentation Slic superpixels compared to state-of-the-art superpixel methods
Reference 1
- Resolution
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Observation 9851c0f0-b79f-4b20-ad8d-0931b766c682 · outbound
This paper cites H., and Seitz, S.
Understanding Deep Learning Techniques for Image Segmentation H., and Seitz, S
Reference 2
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Observation a5264ee9-65a3-4cce-ac3d-86c4968a55e9 · outbound
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Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 3
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Observation 62f5431a-dbf6-432d-a353-14789143867d · outbound
This paper cites I., Zhou, J., Liew, A.
Understanding Deep Learning Techniques for Image Segmentation I., Zhou, J., Liew, A
Reference 4
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Observation 62027650-9db0-44c0-b688-bc9be9e4a04c · outbound
This paper cites an unresolved cited work.
Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 5
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Observation 66a33361-2149-42bc-926b-95807e49fa29 · outbound
This paper cites Classification of breast cancer histology images using convolutional neural networks.
Understanding Deep Learning Techniques for Image Segmentation Classification of breast cancer histology images using convolutional neural networks
Reference 6
- Resolution
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Observation 5ff1c3cb-d247-4af4-bcd6-46f94de7ca67 · outbound
This paper cites Performance com- parison of fpga, gpu and cpu in image processing.
Understanding Deep Learning Techniques for Image Segmentation Performance com- parison of fpga, gpu and cpu in image processing
Reference 7
- Resolution
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Source-reported events for the cited work
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Observation 6d4c0b51-fc27-464d-a22b-b02f555fe2fa · outbound
This paper cites A quality analysis of openstreetmap data.
Understanding Deep Learning Techniques for Image Segmentation A quality analysis of openstreetmap data
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation 5cbe0de9-7833-488f-9e88-dbd7ad5c07d3 · outbound
This paper cites IEEE transactions on pattern analysis and machine intelligence 39 , 12 (2017), 2481–2495.
Understanding Deep Learning Techniques for Image Segmentation IEEE transactions on pattern analysis and machine intelligence 39 , 12 (2017), 2481–2495
Reference 9
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e8f3b8d4-4f1b-48e3-a95d-6a88bbd2cc5c · outbound
This paper cites IEEE transactions on Geoscience and Remote Sensing 45, 5 (2007), 1506– 1511.
Understanding Deep Learning Techniques for Image Segmentation IEEE transactions on Geoscience and Remote Sensing 45, 5 (2007), 1506– 1511
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 4f5b7bc7-6531-4bd2-a003-06504b1558a5 · outbound
This paper cites High spatial resolution satellite imagery, dem derivatives, and image segmentation for the detec- tion of mass wasting processes.
Understanding Deep Learning Techniques for Image Segmentation High spatial resolution satellite imagery, dem derivatives, and image segmentation for the detec- tion of mass wasting processes
Reference 11
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a1d3689a-6418-4682-bc72-c3a1e85bb552 · outbound
This paper cites Color-and texture-based image segmentation using em and its application to content- based image retrieval.
Understanding Deep Learning Techniques for Image Segmentation Color-and texture-based image segmentation using em and its application to content- based image retrieval
Reference 12
- Resolution
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Observation 7e73071e-b881-4ea3-9eee-4196ba0738c7 · outbound
This paper cites Greedy layer-wise training of deep networks.
Understanding Deep Learning Techniques for Image Segmentation Greedy layer-wise training of deep networks
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation ff97c9d3-301b-4039-a5fa-c7c83afbca0c · outbound
This paper cites Learning long-term de- pendencies with gradient descent is difficult.
Understanding Deep Learning Techniques for Image Segmentation Learning long-term de- pendencies with gradient descent is difficult
Reference 14
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation e627b6ba-6db0-4e4a-bdd9-5fda7a428973 · outbound
This paper cites Large scale visual recognition challenge (ilsvrc).
Understanding Deep Learning Techniques for Image Segmentation Large scale visual recognition challenge (ilsvrc)
Reference 15
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 27ecbee6-8153-40d7-867c-9c6fa8ebe0c6 · outbound
This paper cites C., Ehrlich, R., and Full, W.
Understanding Deep Learning Techniques for Image Segmentation C., Ehrlich, R., and Full, W
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation 8f4716df-7012-45eb-b7a6-a05d005a63a7 · outbound
This paper cites S., Fonseca, L.
Understanding Deep Learning Techniques for Image Segmentation S., Fonseca, L
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation c56071c8-cd66-4a08-9cf8-432f2d720547 · outbound
This paper cites What is a salient object? a dataset and a baseline model for salient object detection.
Understanding Deep Learning Techniques for Image Segmentation What is a salient object? a dataset and a baseline model for salient object detection
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 44be22f4-55df-4237-ad66-d470ed739ea5 · outbound
This paper cites Salient Object Detection: A Survey.
Understanding Deep Learning Techniques for Image Segmentation Salient Object Detection: A Survey
Reference 19
- Resolution
- verified exact
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Source-reported events for the cited work
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Observation 729105b8-fb9b-4798-afb9-9deae1cdc593 · outbound
This paper cites Salient object de- tection: A benchmark.
Understanding Deep Learning Techniques for Image Segmentation Salient object de- tection: A benchmark
Reference 20
- Resolution
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Source-reported events for the cited work
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Observation 0f193680-222e-45d7-bfc8-eecbb4a0185c · outbound
This paper cites Fast approximate energy minimization via graph cuts.
Understanding Deep Learning Techniques for Image Segmentation Fast approximate energy minimization via graph cuts
Reference 21
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 0c1520bf-6745-4796-928a-08408d87673d · outbound
This paper cites Y., and Jolly, M.-P.
Understanding Deep Learning Techniques for Image Segmentation Y., and Jolly, M.-P
Reference 22
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 2af0be30-4e65-41b7-82ec-4121da2902c4 · outbound
This paper cites J., Fauqueur, J., and Cipolla, R.
Understanding Deep Learning Techniques for Image Segmentation J., Fauqueur, J., and Cipolla, R
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 22f35541-30d1-4836-9121-a8b1626d0f40 · outbound
This paper cites D., and Ray, L.
Understanding Deep Learning Techniques for Image Segmentation D., and Ray, L
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 082df506-398f-4057-a73a-43ac7f767af5 · outbound
This paper cites L., Magrath, E., Gherman, A., Button, J., Nguyen, J., Bazin, P.-L., Calabresi, P.
Understanding Deep Learning Techniques for Image Segmentation L., Magrath, E., Gherman, A., Button, J., Nguyen, J., Bazin, P.-L., Calabresi, P
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1d268c93-e2f6-452e-abdd-8ab7fce1520b · outbound
This paper cites In Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition (2017), pp.
Understanding Deep Learning Techniques for Image Segmentation In Proceedings of the IEEE Confer- ence on Computer Vision and Pattern Recognition (2017), pp
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 0c96e6fc-7ee0-463f-ae07-54d29c45c09d · outbound
This paper cites Exploiting the self-organizing map for medical image segmentation.
Understanding Deep Learning Techniques for Image Segmentation Exploiting the self-organizing map for medical image segmentation
Reference 27
- Resolution
- verified fuzzy
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Observation 6b8fc017-9dad-4a3f-8af2-c0554d877112 · outbound
This paper cites an unresolved cited work.
Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 28
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Source-reported events for the cited work
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Observation 90c94c86-5565-439e-95a6-3baaca447001 · outbound
This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.
Understanding Deep Learning Techniques for Image Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Reference 29
- Resolution
- verified exact
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Source-reported events for the cited work
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Observation ef452a0d-2654-4a24-8d01-3f8ffa32498c · outbound
This paper cites an unresolved cited work.
Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 30
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Observation 8862cb87-2e5d-4e6c-872a-dd07a3803bdc · outbound
This paper cites an unresolved cited work.
Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 31
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Source-reported events for the cited work
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Observation c1df2222-e899-472d-817e-047d3a41332f · outbound
This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.
Understanding Deep Learning Techniques for Image Segmentation Rethinking Atrous Convolution for Semantic Image Segmentation
Reference 32
- Resolution
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Source-reported events for the cited work
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Observation 5e50a841-8431-4031-a48b-00b95f739633 · outbound
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Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation dac75d52-df59-449c-b489-caae2c8dbf48 · outbound
This paper cites Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation.
Understanding Deep Learning Techniques for Image Segmentation Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Reference 34
- Resolution
- verified exact
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Source-reported events for the cited work
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Observation 29016ad4-4d4c-4654-8610-63238faa0cff · outbound
This paper cites The application of com- petitive hopfield neural network to medical image segmentation.
Understanding Deep Learning Techniques for Image Segmentation The application of com- petitive hopfield neural network to medical image segmentation
Reference 35
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7592cc49-2a83-4cf7-9148-dc4ba20e6c0a · outbound
This paper cites J., Huang, X., and Hu, S.-M.
Understanding Deep Learning Techniques for Image Segmentation J., Huang, X., and Hu, S.-M
Reference 36
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f36e5c8c-3ed6-46bd-92a0-a3bf61b5632b · outbound
This paper cites J., Huang, X., Torr, P.
Understanding Deep Learning Techniques for Image Segmentation J., Huang, X., Torr, P
Reference 37
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 0fc0ce0f-6572-4097-88ae-91ed1f1a59f3 · outbound
This paper cites A multi-cue information based approach to contour detection by utilizing superpixel segmentation.
Understanding Deep Learning Techniques for Image Segmentation A multi-cue information based approach to contour detection by utilizing superpixel segmentation
Reference 38
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 5c3c3a19-e53d-4793-81a4-b6077d719604 · outbound
This paper cites Fuzzy c-means clustering with spatial information for image segmentation.
Understanding Deep Learning Techniques for Image Segmentation Fuzzy c-means clustering with spatial information for image segmentation
Reference 39
- Resolution
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Source-reported events for the cited work
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Observation 8c48f883-b727-4bdd-a3c2-114f6d1ff73c · outbound
This paper cites Robust analysis of feature spaces: color image segmentation.
Understanding Deep Learning Techniques for Image Segmentation Robust analysis of feature spaces: color image segmentation
Reference 40
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 40aefac0-1020-4227-9bd5-bfb45bc0fc95 · outbound
This paper cites The cityscapes dataset for semantic urban scene understanding.
Understanding Deep Learning Techniques for Image Segmentation The cityscapes dataset for semantic urban scene understanding
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.804627Z
Source-reported events for the cited work
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Observation 214d75b6-c37d-48b7-9051-5b6439059141 · outbound
This paper cites Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation.
Understanding Deep Learning Techniques for Image Segmentation Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
Reference 42
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 11b6694f-efdb-41c1-a8e8-3108f92e801d · outbound
This paper cites Instance-aware semantic segmentation via multi-task network cascades.
Understanding Deep Learning Techniques for Image Segmentation Instance-aware semantic segmentation via multi-task network cascades
Reference 43
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation bbb9fff0-ead9-48f8-aa69-f0e98832df04 · outbound
This paper cites R-fcn: Object detection via region- based fully convolutional networks.
Understanding Deep Learning Techniques for Image Segmentation R-fcn: Object detection via region- based fully convolutional networks
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.863528Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c67f0ed5-3cd5-46d6-8c05-737ea853fe50 · outbound
This paper cites Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling.
Understanding Deep Learning Techniques for Image Segmentation Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.349904Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d842284d-9dc9-4299-ae21-7f668d7cbd70 · outbound
This paper cites Combining multilevel contexts of superpixel using con- volutional neural networks to perform natural scene labeling.
Understanding Deep Learning Techniques for Image Segmentation Combining multilevel contexts of superpixel using con- volutional neural networks to perform natural scene labeling
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.615926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 3b08d064-a950-4413-91ab-aae8107ac03f · outbound
This paper cites P., Esquef, I.
Understanding Deep Learning Techniques for Image Segmentation P., Esquef, I
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.656959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 3691c428-171f-4f92-a8e2-1aee92f266b7 · outbound
This paper cites A., and Niessen, W.
Understanding Deep Learning Techniques for Image Segmentation A., and Niessen, W
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.557899Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 34890dfb-1ea6-4246-8999-84997cc48901 · outbound
This paper cites DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images.
Understanding Deep Learning Techniques for Image Segmentation DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
Reference 49
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.453420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c3694b98-9f0a-441f-ad7e-ca8ab556663b · outbound
This paper cites Imagenet: A large-scale hierarchical image database.
Understanding Deep Learning Techniques for Image Segmentation Imagenet: A large-scale hierarchical image database
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.817137Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 4a24efa2-5191-4e3c-967d-ec8413761eaf · outbound
This paper cites Video-based noncooperative iris image segmentation.
Understanding Deep Learning Techniques for Image Segmentation Video-based noncooperative iris image segmentation
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.067931Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 536fe9af-d850-435e-ab49-66a7b35e0348 · outbound
This paper cites W., Xu, D., and Chua, T.-S.
Understanding Deep Learning Techniques for Image Segmentation W., Xu, D., and Chua, T.-S
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.057592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 58e3f18f-7963-4ad7-bdf1-26c6366bd131 · outbound
This paper cites A guide to convolution arithmetic for deep learning.
Understanding Deep Learning Techniques for Image Segmentation A guide to convolution arithmetic for deep learning
Reference 53
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.418232Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f3b9912d-c907-435e-b674-cede160205af · outbound
This paper cites K., Winn, J., and Zisserman, A.
Understanding Deep Learning Techniques for Image Segmentation K., Winn, J., and Zisserman, A
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.060415Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 044ec68b-5463-4fb9-9cd6-4a706a0841ea · outbound
This paper cites Learning hierarchical features for scene labeling.
Understanding Deep Learning Techniques for Image Segmentation Learning hierarchical features for scene labeling
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.955574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ee67e1ab-58b1-4f4d-b843-180c99f4fc8c · outbound
This paper cites F., and Huttenlocher, D.
Understanding Deep Learning Techniques for Image Segmentation F., and Huttenlocher, D
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.063352Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f62ec139-5937-4dbb-b0da-106b4e29a58d · outbound
This paper cites S., and Sensing, R.
Understanding Deep Learning Techniques for Image Segmentation S., and Sensing, R
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.890495Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6e13c395-ace4-41a1-bc36-2906ba57bc4f · outbound
This paper cites M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rud- nicka, A.
Understanding Deep Learning Techniques for Image Segmentation M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rud- nicka, A
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.763292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 75454a64-4890-4122-bb52-950066cc461a · outbound
This paper cites Yet another survey on image segmentation: Region and boundary information integration.
Understanding Deep Learning Techniques for Image Segmentation Yet another survey on image segmentation: Region and boundary information integration
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.827463Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e4ac001e-b894-4cc1-9d0f-47b6a43e5fb2 · outbound
This paper cites Image segmentation in video sequences: A probabilistic approach.
Understanding Deep Learning Techniques for Image Segmentation Image segmentation in video sequences: A probabilistic approach
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.820483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ce4969e1-f9ef-4156-9ba5-c5a4dd2f9f77 · outbound
This paper cites A survey on image segmentation.
Understanding Deep Learning Techniques for Image Segmentation A survey on image segmentation
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.722416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6ad1d040-4da9-47a6-b9ed-0ed9095841eb · outbound
This paper cites Neocognitron: A hierarchical neural network capable of visual pattern recognition.
Understanding Deep Learning Techniques for Image Segmentation Neocognitron: A hierarchical neural network capable of visual pattern recognition
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.074764Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e96d42a8-b6b9-4ad5-a7c8-51f8a4e06a82 · outbound
This paper cites In Compe- tition and cooperation in neural nets.
Understanding Deep Learning Techniques for Image Segmentation In Compe- tition and cooperation in neural nets
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.595529Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ab606929-8fc1-4c9c-b06a-7e933d218870 · outbound
This paper cites A unified video segmentation benchmark: Annotation, metrics and analysis.
Understanding Deep Learning Techniques for Image Segmentation A unified video segmentation benchmark: Annotation, metrics and analysis
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.649576Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 97c04e3f-0f0a-4073-b883-63efb619c82f · outbound
This paper cites Deepirisnet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition.
Understanding Deep Learning Techniques for Image Segmentation Deepirisnet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.133188Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation f6a2abe5-ee2b-4620-81df-7c3477e09cfb · outbound
This paper cites A Review on Deep Learning Techniques Applied to Semantic Segmentation.
Understanding Deep Learning Techniques for Image Segmentation A Review on Deep Learning Techniques Applied to Semantic Segmentation
Reference 66
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.306143Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 2dc58c89-69e4-4583-830e-33589dc062e3 · outbound
This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.
Understanding Deep Learning Techniques for Image Segmentation Are we ready for autonomous driving? the kitti vision benchmark suite
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.800759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 231c21f3-ac70-41b6-8b22-c1732682cca8 · outbound
This paper cites Survey of recent progress in semantic image segmentation with cnns.
Understanding Deep Learning Techniques for Image Segmentation Survey of recent progress in semantic image segmentation with cnns
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.615698Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ffc40863-eb76-4192-9c57-f4b11c303ad1 · outbound
This paper cites Fast R-CNN.
Understanding Deep Learning Techniques for Image Segmentation Fast R-CNN
Reference 69
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.384312Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 52ecdf9a-ec92-4bcd-84a1-1cab826df249 · outbound
This paper cites Rich fea- ture hierarchies for accurate object detection and semantic segmentation.
Understanding Deep Learning Techniques for Image Segmentation Rich fea- ture hierarchies for accurate object detection and semantic segmentation
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.743068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 03750f2a-e6db-46b5-9db9-bc1d3b1ad838 · outbound
This paper cites Generative adversarial nets.
Understanding Deep Learning Techniques for Image Segmentation Generative adversarial nets
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.808507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 9ab7e0b5-8af7-4182-ac78-d3e38c0e7e14 · outbound
This paper cites Decomposing a scene into geometric and semantically consistent regions.
Understanding Deep Learning Techniques for Image Segmentation Decomposing a scene into geometric and semantically consistent regions
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.254835Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e7d24935-780b-459f-b880-1ec8548d6e35 · outbound
This paper cites an unresolved cited work.
Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 73
- Resolution
- unresolved
- raw_fallback, observed 2026-05-24T21:46:24.813343Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation aa64354d-4df1-4c93-b15d-10a35810dc62 · outbound
This paper cites Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method.
Understanding Deep Learning Techniques for Image Segmentation Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method
Reference 74
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.355550Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation bf0da49a-b64b-4367-b735-79dafd14e6f3 · outbound
This paper cites Semantic contours from inverse detectors.
Understanding Deep Learning Techniques for Image Segmentation Semantic contours from inverse detectors
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.144712Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b56c49fd-fab8-4d61-8f81-89cb3cf00507 · outbound
This paper cites Mask r-cnn.
Understanding Deep Learning Techniques for Image Segmentation Mask r-cnn
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.776613Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7bf02040-1aa4-4249-ae84-dc0358b1ef00 · outbound
This paper cites Spatial pyramid pooling in deep convolutional networks for visual recognition.
Understanding Deep Learning Techniques for Image Segmentation Spatial pyramid pooling in deep convolutional networks for visual recognition
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.755196Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 615dee7d-0996-486b-ac45-377d5d649537 · outbound
This paper cites Deep residual learning for image recognition.
Understanding Deep Learning Techniques for Image Segmentation Deep residual learning for image recognition
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.510603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 33a5fa54-a23e-47b5-9385-bced00284461 · outbound
This paper cites E., Osindero, S., and Teh, Y.-W.
Understanding Deep Learning Techniques for Image Segmentation E., Osindero, S., and Teh, Y.-W
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.771431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 9ca135b8-9236-415c-926c-4d37d932f989 · outbound
This paper cites an unresolved cited work.
Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 80
- Resolution
- unresolved
- raw_fallback, observed 2026-05-24T21:46:25.242459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 4e41e9c4-1c81-42b8-8346-4553c9feedae · outbound
This paper cites Long short-term memory.
Understanding Deep Learning Techniques for Image Segmentation Long short-term memory
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.238911Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 654cdd65-5b82-4a93-9bd8-43e5830add34 · outbound
This paper cites Online tracking by learning discriminative saliency map with convolutional neural network.
Understanding Deep Learning Techniques for Image Segmentation Online tracking by learning discriminative saliency map with convolutional neural network
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.165785Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c7852ec4-ee23-450a-b944-22c70bf1cc40 · outbound
This paper cites A fully con- volutional two-stream fusion network for interactive image segmentation.
Understanding Deep Learning Techniques for Image Segmentation A fully con- volutional two-stream fusion network for interactive image segmentation
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.250898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b4ab7163-c77e-4530-931f-5eda0a46dd99 · outbound
This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.
Understanding Deep Learning Techniques for Image Segmentation Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Reference 84
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.316682Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 3ac20136-c68a-45f8-8135-9d2fdcf9531d · outbound
This paper cites Methods for nuclei detection, segmentation, and classification in digital histopathol- ogy: a reviewcurrent status and future potential.
Understanding Deep Learning Techniques for Image Segmentation Methods for nuclei detection, segmentation, and classification in digital histopathol- ogy: a reviewcurrent status and future potential
Reference 85
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.750180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c0859246-ec0a-4b22-b72c-d638a2be0b52 · outbound
This paper cites an unresolved cited work.
Understanding Deep Learning Techniques for Image Segmentation Unresolved cited work
Reference 86
- Resolution
- unresolved
- raw_fallback, observed 2026-05-24T21:46:24.767484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7407878d-f305-40e5-9301-78833401e75f · outbound
This paper cites Hybridization of Otsu Method and Median Filter for Color Image Segmentation.
Understanding Deep Learning Techniques for Image Segmentation Hybridization of Otsu Method and Median Filter for Color Image Segmentation
Reference 87
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.344731Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 562cf478-4616-41b7-b0e3-6a2a314106ae · outbound
This paper cites The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation.
Understanding Deep Learning Techniques for Image Segmentation The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation
Reference 88
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.050472Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 09c6a2f4-b526-4858-834b-39de39f721b7 · outbound
This paper cites D., and Kim, H.
Understanding Deep Learning Techniques for Image Segmentation D., and Kim, H
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.948187Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6aa4e9d6-02cb-4b92-b784-10ad5b3b9d6b · outbound
This paper cites Content based image retrieval through object extraction and querying.
Understanding Deep Learning Techniques for Image Segmentation Content based image retrieval through object extraction and querying
Reference 90
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.246540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 246261f4-dd3a-4ec7-ae10-8d2a6bc4a868 · outbound
This paper cites F., Simpson, J.
Understanding Deep Learning Techniques for Image Segmentation F., Simpson, J
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.611748Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c9065b04-e24d-4bd6-944f-72abce859566 · outbound
This paper cites Unsupervised image segmentation by backpropagation.
Understanding Deep Learning Techniques for Image Segmentation Unsupervised image segmentation by backpropagation
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.125745Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation eb97c844-797e-4a48-b44f-e9e5b9405807 · outbound
This paper cites Dental plaque quan- tification using cellular neural network-based image segmentation.
Understanding Deep Learning Techniques for Image Segmentation Dental plaque quan- tification using cellular neural network-based image segmentation
Reference 93
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.671639Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 19d51dac-0088-4e19-a8c9-a69d0a759d0d · outbound
This paper cites Fingerprint segmentation using cellular neu- ral network.
Understanding Deep Learning Techniques for Image Segmentation Fingerprint segmentation using cellular neu- ral network
Reference 94
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.682646Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 122693a6-8f5a-4602-8c82-13c233ffe0d6 · outbound
This paper cites Fully Convolutional Neural Networks for Crowd Segmentation.
Understanding Deep Learning Techniques for Image Segmentation Fully Convolutional Neural Networks for Crowd Segmentation
Reference 95
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.280070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8a2204cb-072e-43e2-bb14-0bfe0adf81b2 · outbound
This paper cites Snakes: Active contour models.
Understanding Deep Learning Techniques for Image Segmentation Snakes: Active contour models
Reference 96
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:25.116774Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 17c726c6-e8b3-4ec1-8355-3a2b265b6e80 · outbound
This paper cites Adam: A Method for Stochastic Optimization.
Understanding Deep Learning Techniques for Image Segmentation Adam: A Method for Stochastic Optimization
Reference 97
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.463790Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7dc48cb7-eaad-41c7-bb9b-d8d08564b93c · outbound
This paper cites Auto-Encoding Variational Bayes.
Understanding Deep Learning Techniques for Image Segmentation Auto-Encoding Variational Bayes
Reference 98
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:46:24.448448Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 4a30eae2-c15c-4b7f-a642-b1726a4798fe · outbound
This paper cites Hypernet: Towards accu- rate region proposal generation and joint object detection.
Understanding Deep Learning Techniques for Image Segmentation Hypernet: Towards accu- rate region proposal generation and joint object detection
Reference 99
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.912019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation fc29be8a-22f3-4b4a-8456-088fb20959e6 · outbound
This paper cites Efficient inference in fully connected crfs with gaussian edge potentials.
Understanding Deep Learning Techniques for Image Segmentation Efficient inference in fully connected crfs with gaussian edge potentials
Reference 101
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-24T21:46:24.963074Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Pith citing papers
No inbound Pith citation observations are available.