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Paper Citation Record · LEDGER

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features

As of 22 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:1908.06537.

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

pith.paper-citation-record.v1
1908.06537 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:46:00.290506Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

54 of 54 outbound references displayed

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  • verified fuzzy49
  • unresolved5
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 954e36e2-9c87-49ab-b6fb-5041fbf7b56c · outbound

This paper cites Dense semantic correspondence where every pixel is a classifier.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Dense semantic correspondence where every pixel is a classifier

Reference 1

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Observation c9941350-1d81-469f-a302-27418f6ccd8c · outbound

This paper cites Detect what you can: De- tecting and representing objects using holistic models and body parts.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Detect what you can: De- tecting and representing objects using holistic models and body parts

Reference 2

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verified fuzzy
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Observation b5605077-c9c7-4bed-abbb-5fee935899d4 · outbound

This paper cites Learning graphs to match.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Learning graphs to match

Reference 3

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Observation 54a708da-dbf2-49a9-a59a-2a4328614895 · outbound

This paper cites Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Unsupervised object discovery and localization in the wild: Part-based matching with bottom-up region proposals

Reference 4

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Observation 6e3162b4-adfa-490c-8e00-a256280a50c0 · outbound

This paper cites Universal correspondence network.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Universal correspondence network

Reference 5

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Source-reported events for the cited work

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Observation eb8b006f-9130-428c-9e26-cd4af30ae479 · outbound

This paper cites Histograms of oriented gradi- ents for human detection.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Histograms of oriented gradi- ents for human detection

Reference 6

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Source-reported events for the cited work

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Observation 28a4cb2f-ceae-44c1-a2e6-33529a5ac3b9 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Imagenet: A large-scale hierarchical image database

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation fda83724-5f6e-4db6-bc53-1754afbc7292 · outbound

This paper cites Approximate thin plate spline mappings.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Approximate thin plate spline mappings

Reference 8

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Source-reported events for the cited work

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Observation d272c252-e877-4895-b9da-687b2242ecfa · outbound

This paper cites an unresolved cited work.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Unresolved cited work

Reference 9

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Source-reported events for the cited work

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Observation a284438e-8bf6-4e95-8a54-eec3abca65c2 · outbound

This paper cites Hierarchical metric learning and matching for 2d and 3d geometric correspon- dences.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Hierarchical metric learning and matching for 2d and 3d geometric correspon- dences

Reference 10

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verified fuzzy
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Source-reported events for the cited work

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Observation 45e4e7bc-1560-4832-bd37-2484561e3f26 · outbound

This paper cites Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Learning gener- ative visual models from few training examples: An incre- mental bayesian approach tested on 101 object categories

Reference 11

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Source-reported events for the cited work

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Observation fdd2fbda-79a3-470a-8ccd-592c74b204f8 · outbound

This paper cites Deformable part models are convolutional neural net- works.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Deformable part models are convolutional neural net- works

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 155fd1d6-52dd-425d-824b-80714360564f · outbound

This paper cites Caltech-256 object category dataset.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Caltech-256 object category dataset

Reference 13

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Source-reported events for the cited work

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Observation a95cee06-ad15-4403-b88d-332ccfb1b866 · outbound

This paper cites Proposal flow.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Proposal flow

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 52854131-60bf-4c2e-a20a-0cad9cf13ae5 · outbound

This paper cites Proposal flow: Semantic correspondences from ob- ject proposals.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Proposal flow: Semantic correspondences from ob- ject proposals

Reference 15

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 01a7f0b3-3779-4b11-a055-6176e22039e5 · outbound

This paper cites Scnet: Learning semantic correspondence.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Scnet: Learning semantic correspondence

Reference 16

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 3150a1de-aca6-4b24-8200-9f8d6037dbf3 · outbound

This paper cites Semantic contours from inverse detectors.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Semantic contours from inverse detectors

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 845f3a96-0bbb-49fd-9699-507dffe06f3e · outbound

This paper cites Hypercolumns for object segmentation and fine-grained localization.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Hypercolumns for object segmentation and fine-grained localization

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 4c1ffce0-a007-4601-93bf-f492c5c69508 · outbound

This paper cites Deep residual learning for image recognition.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Deep residual learning for image recognition

Reference 19

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ba756c4f-00a2-4539-bf82-f8938ab7e6eb · outbound

This paper cites Parn: Pyramidal affine regression net- works for dense semantic correspondence.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Parn: Pyramidal affine regression net- works for dense semantic correspondence

Reference 20

Resolution
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Source-reported events for the cited work

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Observation 0109daa5-393a-4b69-9ee5-1a72efdb0393 · outbound

This paper cites Warpnet: Weakly supervised matching for single- view reconstruction.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Warpnet: Weakly supervised matching for single- view reconstruction

Reference 21

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 03c790ec-605c-43e6-9ce5-8675005bb4ce · outbound

This paper cites De- formable spatial pyramid matching for fast dense correspon- dences.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features De- formable spatial pyramid matching for fast dense correspon- dences

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 2026-08-22T06:32:14.747728+00:00.

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Observation 9c35aa14-f297-4780-a455-c9575f5e2cff · outbound

This paper cites Recurrent transformer networks for semantic correspondence.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Recurrent transformer networks for semantic correspondence

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 2026-08-22T06:32:14.747728+00:00.

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Observation 7e040368-71c1-4629-bc38-d0d931ff0578 · outbound

This paper cites Fcss: Fully con- volutional self-similarity for dense semantic correspondence.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Fcss: Fully con- volutional self-similarity for dense semantic correspondence

Reference 24

Resolution
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Observation f67415ea-c565-481c-b338-69268e1f096f · outbound

This paper cites Dctm: Discrete-continuous transforma- tion matching for semantic flow.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Dctm: Discrete-continuous transforma- tion matching for semantic flow

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 2026-08-22T06:32:14.747728+00:00.

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Observation 20826d8b-c51b-43a2-a423-cbaabcdd30c8 · outbound

This paper cites Hypernet: Towards accurate region proposal generation and joint object detection.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Hypernet: Towards accurate region proposal generation and joint object detection

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 2026-08-22T06:32:14.747728+00:00.

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Observation b87e81ae-60f1-48ef-800d-b0f36f194449 · outbound

This paper cites Imagenet classification with deep convolutional neural net- works.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Imagenet classification with deep convolutional neural net- works

Reference 27

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 0c08261c-4ecc-49b8-9754-a71f634cc1c9 · outbound

This paper cites Semantic Matching by Weakly Supervised 2D Point Set Registration.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Semantic Matching by Weakly Supervised 2D Point Set Registration

Reference 28

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Source-reported events for the cited work

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Observation 399c6b6a-58c6-4d81-be7a-a71a36fa9689 · outbound

This paper cites One-shot learning of object categories.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features One-shot learning of object categories

Reference 29

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation c58c75ac-127c-4f07-9913-5c68255df7ce · outbound

This paper cites Feature pyramid networks for object detection.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Feature pyramid networks for object detection

Reference 30

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 2201dd96-f23d-4e93-9b5c-f777bd94432b · outbound

This paper cites Jointly optimizing 3d model fitting and fine-grained classification.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Jointly optimizing 3d model fitting and fine-grained classification

Reference 31

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 4a31cd6b-39fe-4ba3-b84d-c7793d022839 · outbound

This paper cites Sift flow: Dense correspondence across scenes and its applications.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Sift flow: Dense correspondence across scenes and its applications

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 67625399-14be-41c0-81e4-eb52188d9776 · outbound

This paper cites DARTS: Differentiable architecture search.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features DARTS: Differentiable architecture search

Reference 33

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 8163e004-28b3-4798-accb-7e99770c5716 · outbound

This paper cites Do con- vnets learn correspondence? In Proc.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Do con- vnets learn correspondence? In Proc

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation cf52457f-999f-4ce9-99a0-120c22c7d037 · outbound

This paper cites Prime object proposals with randomized prim’s algorithm.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Prime object proposals with randomized prim’s algorithm

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.591370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.207838Z digest=sha256:11818efb4776472a97300913408d13a805e0ed7bd2bcf881ad6a2ab9556c7950

Observation 680d63c0-8aa9-46c9-b75b-f94c5fd0dc82 · outbound

This paper cites Medress, F.S.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Medress, F.S

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.579643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.212132Z digest=sha256:d8b286187ec92b713b153b99927dc42597ed901f73fcc01d83ffc57cdb132b46

Observation 7d9f69b1-d188-4ed3-a5a8-0d4d3af1cea9 · outbound

This paper cites Self-supervised learning of geometrically stable features through probabilistic introspection.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Self-supervised learning of geometrically stable features through probabilistic introspection

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.567304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.217541Z digest=sha256:444a412bce2e52c8b763020bc3feac3661b5bdcfbd43f5cbae819ad5976d9aec

Observation de4c1e97-17e7-4a42-bcb0-83063dcbe498 · outbound

This paper cites I have seen enough: Transferring parts across categories.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features I have seen enough: Transferring parts across categories

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.555935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.222336Z digest=sha256:845d1cf3d3f8430f1553f9b45bfd72ff160ea8b5bf8c417598446a0f9f19a8bb

Observation b8ed8c28-5479-4af0-991f-a7f35cc8be98 · outbound

This paper cites An- chornet: A weakly supervised network to learn geometry- sensitive features for semantic matching.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features An- chornet: A weakly supervised network to learn geometry- sensitive features for semantic matching

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.543540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.226908Z digest=sha256:cafb23b8887069b95859b1579ad60b1dba0a04d4eb8752ae46e1c94a1f07845a

Observation 0b9fe979-657f-4f13-87a1-82c29bfdcdb4 · outbound

This paper cites Multiscale combinatorial grouping for image segmentation and object proposal gener- ation.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Multiscale combinatorial grouping for image segmentation and object proposal gener- ation

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.531060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.230892Z digest=sha256:a5f3be1bf5562db013214c6e2fe51cee9c395ef332af4435b5c481733803665c

Observation a9d43556-4e5a-4c7b-ba81-dc858e102d13 · outbound

This paper cites Convo- lutional neural network architecture for geometric matching.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Convo- lutional neural network architecture for geometric matching

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.518803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.235251Z digest=sha256:305b9ad2fdf088c19527186ec830e83ecdac126e74fe2d10114c25a5693fea3c

Observation 868c8cea-0a85-4ac1-bf83-d42fe626ae03 · outbound

This paper cites End-to- end weakly-supervised semantic alignment.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features End-to- end weakly-supervised semantic alignment

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.506599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.239759Z digest=sha256:916eb85c1ff61a5b39caba1c8091f4bcea2ac6b4c22a8a7d8099f0880fec13c6

Observation 94ce9214-163d-45f9-b541-aef2634f7f76 · outbound

This paper cites Neighbourhood con- sensus networks.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Neighbourhood con- sensus networks

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.492498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.244619Z digest=sha256:475a16053ddbfa5e28c57872f98f19dd16bd73550e7cf60070fa6e05a0004e75

Observation 3ad9d188-d572-44b7-ae3e-200e6f5e78f2 · outbound

This paper cites Unsupervised joint object discovery and segmentation in internet images.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Unsupervised joint object discovery and segmentation in internet images

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.478953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.248656Z digest=sha256:c422aac2ee9ba59212e2cfce777cc765c04c60aa825efa0aefc3538caa018933

Observation 09fbf33c-815d-4f6d-ba99-899b90fc2466 · outbound

This paper cites Attentive semantic alignment with offset-aware correlation kernels.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Attentive semantic alignment with offset-aware correlation kernels

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.464353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.253140Z digest=sha256:126c05a50749f25063937113cd74d337c077a470254cc828ef1bed2b6363f9f9

Observation bfad372e-89df-44f5-a946-91ea4276f0bb · outbound

This paper cites Joint re- covery of dense correspondence and cosegmentation in two images.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Joint re- covery of dense correspondence and cosegmentation in two images

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.452012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.257027Z digest=sha256:123387ad46d51db2800df4b58839d270c40367557a2db6b0bd3919948a241217

Observation a2771d98-bbf4-41a8-b830-5cc878c95c14 · outbound

This paper cites Deep semantic feature matching.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Deep semantic feature matching

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.439471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.261489Z digest=sha256:d68292cdf166a722b0839491ae743b398557489991f81804e111d2f3396d196a

Observation d1559bcb-2ee0-4a4b-9710-33283ba81241 · outbound

This paper cites Selective search for ob- ject recognition.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Selective search for ob- ject recognition

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.426408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.265697Z digest=sha256:c42c444111ea680adfe212af549237b775bac77f7d0b0f37a1d3f32e99c68e39

Observation 38230954-7a18-439b-b651-d447f2469f87 · outbound

This paper cites an unresolved cited work.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Unresolved cited work

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-14T12:46:00.269344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:46:00.269344Z digest=sha256:beabc7f0998a197a121c8bc3fb3252c8ef1a1d0014b517bd7c56fb68e9bd157e

Observation aad45cb2-8e30-4265-b0e7-738d323c020e · outbound

This paper cites Beyond pascal: A benchmark for 3d object detection in the wild.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Beyond pascal: A benchmark for 3d object detection in the wild

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.404426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.273205Z digest=sha256:2f109bba8380eeb9b2f2af294b08afd63bc46fa9ccad9a25f14f96b83a262dbf

Observation bab47ca8-2e5d-47d5-8571-d99a63fec14b · outbound

This paper cites Exploring Randomly Wired Neural Networks for Image Recognition.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Exploring Randomly Wired Neural Networks for Image Recognition

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-14T12:46:00.277262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:46:00.277262Z digest=sha256:396644db65dfde303251ecd6ef5f5a01d5288d2c442f37a048f064a122c1f7d5

Observation cc2dff21-df04-45ad-9615-db9fe5a4f59a · outbound

This paper cites FlowWeb: Joint image set alignment by weaving con- sistent, pixel-wise correspondences.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features FlowWeb: Joint image set alignment by weaving con- sistent, pixel-wise correspondences

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.388879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.282092Z digest=sha256:f217e318c4a2e17494cb9dcc5e0e368cb2160780ed05dee079e15b8bf7c305cc

Observation 166d10f7-757a-4b18-8699-be3edaaaeb47 · outbound

This paper cites Neural architecture search with reinforcement learning.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Neural architecture search with reinforcement learning

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T12:46:00.373850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.286410Z digest=sha256:782c468a471228f9eaf726e4c8b4843d8ae1ded53ec7e6d9dfa5614480e105ea

Observation 8da0d8ef-8828-400c-9390-3e24a9a2bdb2 · outbound

This paper cites an unresolved cited work.

Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-14T12:46:00.358177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T12:46:00.290506Z digest=sha256:e9391f5c0c571c3bf7fef37409c76aacee4f8be62ba81e9e35401ea776286708

Pith citing papers

No inbound Pith citation observations are available.