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Paper Citation Record · LEDGER

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection

As of 12 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 0 inbound Pith citation observations for arXiv:2412.01556.

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

pith.paper-citation-record.v1
2412.01556 v1

Coverage vector

measured 81 of 81 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:21:07.783291Z

measured 81 of 81 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

81 of 81 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7090a0af-a775-43ab-acd3-8ef451166b47 · outbound

This paper cites Multi-modal interactive attention and dual progressive decoding network for RGB-D/T salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Multi-modal interactive attention and dual progressive decoding network for RGB-D/T salient object detection,

Reference 1

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Observation 3c48cd6b-d683-431c-8678-0ab69c2ef63f · outbound

This paper cites Multi-interactive dual- decoder for rgb-thermal salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Multi-interactive dual- decoder for rgb-thermal salient object detection,

Reference 2

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Observation 6ffe8057-5a7d-49ef-9537-36e738979610 · outbound

This paper cites A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression,

Reference 3

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Observation ec07f214-f600-46b5-b0ec-c1a606e48ebb · outbound

This paper cites Saliency-aware video object segmentation,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Saliency-aware video object segmentation,

Reference 4

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Observation 6f80ac6a-661e-4426-9356-3961fde4b095 · outbound

This paper cites Non-rigid object tracking via deep multi-scale spatial-temporal discriminative saliency maps,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Non-rigid object tracking via deep multi-scale spatial-temporal discriminative saliency maps,

Reference 5

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Observation 2724141b-01aa-4634-a46d-3deb6679c945 · outbound

This paper cites Context disentan- gling and prototype inheriting for robust visual grounding,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Context disentan- gling and prototype inheriting for robust visual grounding,

Reference 6

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Source-reported events for the cited work

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Observation c40e7b45-a490-475a-816c-206287a4f0db · outbound

This paper cites Salient object detection: A survey,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Salient object detection: A survey,

Reference 7

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Observation 123c13cf-9b90-4ddf-b7ad-142cb7b8a869 · outbound

This paper cites Salient object detection in the deep learning era: An in-depth survey,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Salient object detection in the deep learning era: An in-depth survey,

Reference 8

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Source-reported events for the cited work

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Observation 7527f0df-b29a-43a2-aee0-7548b51d6337 · outbound

This paper cites Rethinking RGB-D salient object detection: Models, data sets, and large-scale benchmarks,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Rethinking RGB-D salient object detection: Models, data sets, and large-scale benchmarks,

Reference 9

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Source-reported events for the cited work

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Observation f983a047-cf65-42e1-bcd8-3c865470c3b1 · outbound

This paper cites RGB-D salient object detection: A survey,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection RGB-D salient object detection: A survey,

Reference 10

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Source-reported events for the cited work

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Observation 314f45d7-d60e-4b0a-a77a-8ddc7092254d · outbound

This paper cites Robust RGB-D fusion for saliency detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Robust RGB-D fusion for saliency detection,

Reference 11

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Observation cd599fa1-1ceb-4dd4-b5ae-6e42dae56485 · outbound

This paper cites Siamese network for RGB-D salient object detection and beyond,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Siamese network for RGB-D salient object detection and beyond,

Reference 12

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Observation 934ba702-bc13-42b9-b510-1ced2fbb99cc · outbound

This paper cites Depth quality- inspired feature manipulation for efficient RGB-D salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Depth quality- inspired feature manipulation for efficient RGB-D salient object detection,

Reference 13

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Source-reported events for the cited work

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Observation c316c76b-2d03-4955-a18d-fe0982048aba · outbound

This paper cites RGB-T saliency detection benchmark: Dataset, baselines, analysis and a novel approach,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection RGB-T saliency detection benchmark: Dataset, baselines, analysis and a novel approach,

Reference 14

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Source-reported events for the cited work

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Observation 151308cd-0028-457f-b67f-8aa9bfd7ac28 · outbound

This paper cites Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation b7586dcc-424f-4dce-bce1-eb2230f86824 · outbound

This paper cites RGBT Salient Object Detection: A Large-scale Dataset and Benchmark.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

Reference 16

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Observation 949d7dd0-d842-4ca7-8c8f-49c6c98482e4 · outbound

This paper cites Efficient context- guided stacked refinement network for rgb-t salient object detec- tion,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Efficient context- guided stacked refinement network for rgb-t salient object detec- tion,

Reference 17

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Observation 9fb18c78-d29e-43c4-a287-68d30a39e432 · outbound

This paper cites Ecffnet: Effective and consistent feature fusion network for rgb-t salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Ecffnet: Effective and consistent feature fusion network for rgb-t salient object detection,

Reference 18

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Observation 78396864-21a3-4afb-8d9a-1a7b6c540b51 · outbound

This paper cites Cgfnet: Cross- guided fusion network for RGB-T salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Cgfnet: Cross- guided fusion network for RGB-T salient object detection,

Reference 19

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Observation 9de37536-9ce4-4788-b30f-7c3a5369dc62 · outbound

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Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Unresolved cited work

Reference 20

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Observation 1c717849-4483-4a2d-badd-31097a84ae8a · outbound

This paper cites Separate visual pathways for perception and action,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Separate visual pathways for perception and action,

Reference 21

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Observation 04f763bf-3458-4164-97ee-6629edbc9eee · outbound

This paper cites Cognitive neuroscience: feedback for natural visual stimuli,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Cognitive neuroscience: feedback for natural visual stimuli,

Reference 22

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Observation d02155b3-131a-454c-9b17-ab13d309d44f · outbound

This paper cites RGB-T image saliency detection via collaborative graph learning,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection RGB-T image saliency detection via collaborative graph learning,

Reference 23

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Observation 68df21e4-a18b-4c40-b5ff-26755b52c438 · outbound

This paper cites Saliency detection via dense and sparse reconstruction,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Saliency detection via dense and sparse reconstruction,

Reference 24

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Observation ad7a68c4-6c11-4e79-937e-c8b081a0b8f5 · outbound

This paper cites Global contrast based salient region detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Global contrast based salient region detection,

Reference 25

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0f4fb204-3e90-4c5c-ab17-0e16f53e54aa · outbound

This paper cites Salient object detection: A discriminative regional feature integration approach,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Salient object detection: A discriminative regional feature integration approach,

Reference 26

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Source-reported events for the cited work

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Observation c14b1e33-979a-4d68-b61b-8d547feeb384 · outbound

This paper cites Submodular salient region detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Submodular salient region detection,

Reference 27

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Source-reported events for the cited work

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Observation 1348d898-d5f7-40ef-a3af-d9e5d86caec3 · outbound

This paper cites Saliency optimization from robust background detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Saliency optimization from robust background detection,

Reference 28

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 2279156b-263a-4ecb-b310-d85b02da1974 · outbound

This paper cites Salient region detection via integrating diffusion-based compactness and local contrast,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Salient region detection via integrating diffusion-based compactness and local contrast,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation fec76941-adbc-4a58-8137-49d30394218c · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Very deep convolutional networks for large-scale image recognition,

Reference 30

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Source-reported events for the cited work

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Observation 1f9dd3bc-328f-4d9e-82f1-538fc3ef90ae · outbound

This paper cites Deep residual learning for image recognition,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Deep residual learning for image recognition,

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation d770648b-b034-45a9-82d7-d39955c82771 · outbound

This paper cites Deeply supervised salient object detection with short connections,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Deeply supervised salient object detection with short connections,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 058910a7-4b75-437d-8a5e-2ce4f6ce54aa · outbound

This paper cites Progressive attention guided recurrent network for salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Progressive attention guided recurrent network for salient object detection,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d63cf4fa-5034-47c0-948d-b13e78f94937 · outbound

This paper cites Res2net: A new multi-scale backbone architecture,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Res2net: A new multi-scale backbone architecture,

Reference 34

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raw_fallback, observed 2026-08-12T04:21:08.228934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.646117Z digest=sha256:339bc4cb4aecc88ae6b529772fc9bf2a495ad2e029a619a17dc6ce7b9e6bf116

Observation c2fcdd4c-5c33-47ea-9f36-c89add8fcf0b · outbound

This paper cites Cascaded partial decoder for fast and accurate salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Cascaded partial decoder for fast and accurate salient object detection,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.221079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.648514Z digest=sha256:1f27d19ea06820f5e69764c703fa435290ac1c25b23dfa43015d44be0c4f851d

Observation c57f9517-b5e6-4702-85d2-6b7481abbc69 · outbound

This paper cites F3net: Fusion, feedback and focus for salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection F3net: Fusion, feedback and focus for salient object detection,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.211151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.651620Z digest=sha256:2db81ace446163b9f85bda97de66b2da5e25a8bc3e53a9bff7905eebf172f4fe

Observation fdbdeabf-61bc-487e-853c-2305b4025769 · outbound

This paper cites Global context-aware progressive aggregation network for salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Global context-aware progressive aggregation network for salient object detection,

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-12T04:21:07.654196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:21:07.654196Z digest=sha256:3437ef2f78dd7db7ae06688ecb66fffde03eaad46a9622779f673e245c907df6

Observation 70aaa720-a600-4380-9346-7063eeb8bd99 · outbound

This paper cites Label decoupling framework for salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Label decoupling framework for salient object detection,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.196217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.657158Z digest=sha256:4aa08484bb4cce58a0cafb0247e27a108d5e705333eb8e04bb021361a1088cb4

Observation 10134b90-00e4-4d12-a49d-8968f60ffa67 · outbound

This paper cites Auto-msfnet: Search multi-scale fusion network for salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Auto-msfnet: Search multi-scale fusion network for salient object detection,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.187055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.660165Z digest=sha256:283819ab09d9d7b906e519d3c7d72cb538220dac6f1e64a689fcafdbb8cd5e3c

Observation 23f3bec7-13a7-44fc-8516-186e8e579b8f · outbound

This paper cites Multi-scale interactive network for salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Multi-scale interactive network for salient object detection,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.178759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.663809Z digest=sha256:3f4edaf0ae636178c68be367b9c536c19df7e9c5edcc899fdbf276d55ff72329

Observation 74de52c7-2c9c-42f4-9b59-9deac7f49318 · outbound

This paper cites Cross-modality discrepant interaction network for RGB-D salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Cross-modality discrepant interaction network for RGB-D salient object detection,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.169940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.666926Z digest=sha256:6c2237f211bcf9e992f548bacd584de55b3ec8f4decb2f7eb4ad61b278695643

Observation ffbcbbb8-a08b-4488-bed7-82475009af3d · outbound

This paper cites Learning discriminative cross- modality features for RGB-D saliency detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Learning discriminative cross- modality features for RGB-D saliency detection,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.160153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.670007Z digest=sha256:143315dc6a84f60fd9d2e046ff37eab21db331997ff7f6ea77e6494231623392

Observation a35e1954-6a19-4a10-b9e2-eb7c8c393e9e · outbound

This paper cites M3s-nir: Multi-modal multi-scale noise-insensitive ranking for rgb-t saliency detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection M3s-nir: Multi-modal multi-scale noise-insensitive ranking for rgb-t saliency detection,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.150557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.672722Z digest=sha256:bfd16f6320b6eacab1f233dcd7b7374f5722abf91bd4b0ae23037d36d831711a

Observation a6d8c1da-b853-4691-80e8-a0b326e96e23 · outbound

This paper cites Learning multiscale deep features and SVM regressors for adaptive RGB-T saliency detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Learning multiscale deep features and SVM regressors for adaptive RGB-T saliency detection,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.139103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.675655Z digest=sha256:467cc641fe11c0de0968a5c0d9ac7278e4f07899aad22cd13e89c4d565c697ce

Observation f8097fb0-84ff-4700-ba5d-4a5765057ab8 · outbound

This paper cites RGB-T salient object detection via fusing multi-level CNN features,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection RGB-T salient object detection via fusing multi-level CNN features,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.131278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.678961Z digest=sha256:d3ac6e5a34c5abf170c8269735c28dce01a912d49ff10c16b0db9dfc996e4987

Observation 9ce8eb04-4dce-4e48-ab3b-a679b39ebbd2 · outbound

This paper cites Revisiting feature fusion for RGB-T salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Revisiting feature fusion for RGB-T salient object detection,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.123323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.682103Z digest=sha256:5b10e5164ecd74186fd1d7a2fe5dd72f2582ee08958173fed5cc8065d65aeffd

Observation 1934a666-adde-46f6-afa0-ecabf2dcffa1 · outbound

This paper cites Specificity- preserving RGB-D saliency detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Specificity- preserving RGB-D saliency detection,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.113048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.685377Z digest=sha256:c0462ebb0cc4e0a562c52920cc1cc14c13a7ab24954ac2235a1f926cf36db1d9

Observation e0039751-747d-4fa3-a2cf-2f19bd5bba54 · outbound

This paper cites Cir-net: Cross-modality interaction and refinement for RGB-D salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Cir-net: Cross-modality interaction and refinement for RGB-D salient object detection,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.102067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.687817Z digest=sha256:4034dcf7b4c0e6ba1b7976c07775e82d2aa4e223f988c723e4f328e752fc5f2d

Observation 575cc98a-d8e8-4080-b5ed-625ab452096c · outbound

This paper cites an unresolved cited work.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Unresolved cited work

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-12T04:21:07.690953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:21:07.690953Z digest=sha256:0181b489614a65f0fbd710d66e22d20cd1b4374b2f0e39a3b030623d9b577a40

Observation f64ea89a-730a-4be7-a5e4-6fed76a5ba55 · outbound

This paper cites Segregation of form, color, move- ment, and depth: anatomy, physiology, and perception,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Segregation of form, color, move- ment, and depth: anatomy, physiology, and perception,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.086775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.693476Z digest=sha256:e8f2b6999f0cb1adab61ba58999c00e2494cb61a17f72d3b9c0659326874740d

Observation 7b952501-cdfa-4a73-877f-abd5ef9b91aa · outbound

This paper cites CBAM: convolutional block attention module,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection CBAM: convolutional block attention module,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.078746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.696447Z digest=sha256:1a1726c8f136d12193c594d1b8a5c0240ef3f7cc485fbdab82e2d560293d05d1

Observation 30b1d5ee-c670-409b-87d3-0ea944a1f15a · outbound

This paper cites Ctnet: Context-based tandem network for semantic segmentation,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Ctnet: Context-based tandem network for semantic segmentation,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.070633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.699283Z digest=sha256:650f1cca6d22f865ad70982a3eb7634f20def2058980805fd6818b512e8addb3

Observation 997e87ab-f9cd-4656-9c58-c6d4c9f0fe2f · outbound

This paper cites A tutorial on the cross-entropy method,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection A tutorial on the cross-entropy method,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.062476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.702822Z digest=sha256:42e82da423feab3cbbb3e6d05aca34cef5c626ad354339d2fd9f9c1c7832ee1f

Observation a350a1de-661f-4f28-b894-12e8124c4e52 · outbound

This paper cites Deeproadmapper: Extracting road topology from aerial images,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Deeproadmapper: Extracting road topology from aerial images,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.055492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.706012Z digest=sha256:e2957bc58179158f21bd81c039e871b20955ab805016bd8a26d18a0606ebc77a

Observation e5d35f33-869e-4b90-85eb-e84ea48faf33 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.045236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.709197Z digest=sha256:f8131229e435fc88ece715448f6aea545a884f280ed84ce6b16b971ff7078161

Observation 0eb8e49c-109d-445c-95a8-ebc503c3fda7 · outbound

This paper cites Learning selective self-mutual attention for RGB-D saliency detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Learning selective self-mutual attention for RGB-D saliency detection,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.036069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.712246Z digest=sha256:8d15b6caca427a9fa2d4ec57110c3dcf071ec90b51f288f5fdf1b3941852a695

Observation 5ba86826-4668-475f-86c3-7fb424cd8994 · outbound

This paper cites JL-DCF: joint learning and densely-cooperative fusion framework for RGB-D salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection JL-DCF: joint learning and densely-cooperative fusion framework for RGB-D salient object detection,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.026288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.715528Z digest=sha256:3c90b55f78d1d373cb8972f2ae3213ecf7ab50fa906593ea6ed38c8eba2b5185

Observation 7572c992-6e52-4850-80fa-1a843af3b9c9 · outbound

This paper cites Hierarchical alternate interaction network for RGB-D salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Hierarchical alternate interaction network for RGB-D salient object detection,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.015856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.718041Z digest=sha256:9f5abbd283264deac92a0f380befdfc86a6b4fc9af8d0ea6771c712151aa11c7

Observation c0f2b177-74f2-47c5-9037-518f884809ea · outbound

This paper cites Three-stage bidirectional interaction network for efficient RGB-D salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Three-stage bidirectional interaction network for efficient RGB-D salient object detection,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:08.005620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.720897Z digest=sha256:72dd2f4bae6e81fedb930b336c62f57432ec6e9f2609dde3898777f95f45d978

Observation 5385eb11-4b04-4a81-87b3-53a9d90527b6 · outbound

This paper cites Source-free depth for object pop-out,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Source-free depth for object pop-out,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.994756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.723751Z digest=sha256:c0455798491135e326eb90a68f53d42585f0e162d51d09012675fe5d83fc7b4a

Observation c9eb1033-5198-4b3b-8f0d-59d153a8ce88 · outbound

This paper cites Hidanet: RGB-D salient object detection via hierarchical depth awareness,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Hidanet: RGB-D salient object detection via hierarchical depth awareness,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.987392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.726342Z digest=sha256:2149b95dba0934a2dca3803cf7a10bb5f0a590de438834590ba27f49f7e10887

Observation b12f7f82-0c95-4f98-b37c-83ed8138f92c · outbound

This paper cites Unified information fusion network for multi-modal RGB-D and RGB-T salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Unified information fusion network for multi-modal RGB-D and RGB-T salient object detection,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.979673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.729244Z digest=sha256:ffb24a0d7d7076d97f3644701b3ab0e32b0f0c575541c359faee112fe1cc779c

Observation 80a090c8-2ace-4989-b6ed-ded04f4b1047 · outbound

This paper cites Real-time one-stream semantic-guided refinement network for rgb-thermal salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Real-time one-stream semantic-guided refinement network for rgb-thermal salient object detection,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.971082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.731912Z digest=sha256:70147aabeda564f2cfd9a8bf40a6b85807fbad4cb16b57d821350b5245a8af77

Observation 21f37bd4-432e-4372-a3d4-d8d228c50501 · outbound

This paper cites Lsnet: Lightweight spatial boosting network for detecting salient objects in rgb-thermal images,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Lsnet: Lightweight spatial boosting network for detecting salient objects in rgb-thermal images,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.962282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.734368Z digest=sha256:d9570bdbb294a1a662d3c915e588861a8defa5ce3c378e3bf66be3c0ea430658

Observation b2a8e6f5-abdf-40ab-83d4-fde9e145a656 · outbound

This paper cites CAVER: cross-modal view- mixed transformer for bi-modal salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection CAVER: cross-modal view- mixed transformer for bi-modal salient object detection,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.952102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.736822Z digest=sha256:7e8b43eb7323e2d29f24ca48d12f2b387e846b8a07b64fafa3c735c54b691d52

Observation 0b41c6bb-2bf1-4dd0-b8f7-d7f8fe4e5f72 · outbound

This paper cites Tritransnet: RGB- D salient object detection with a triplet transformer embedding network,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Tritransnet: RGB- D salient object detection with a triplet transformer embedding network,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.942478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.739858Z digest=sha256:445058f163c9957295cb7c36c57a15d69d5f26906ed9ecc06c19da1d2f27473b

Observation b8e595d2-c957-4fe3-b913-abe7814a20c9 · outbound

This paper cites Swinnet: Swin transformer drives edge-aware RGB-D and RGB-T salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Swinnet: Swin transformer drives edge-aware RGB-D and RGB-T salient object detection,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.934019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.742818Z digest=sha256:77cbe37dcc04bddcf31d4b3d4ad55feb047f4476dba942db6adeb5e158b240d3

Observation c97def86-ce01-4685-bfb7-d342743484ba · outbound

This paper cites Hrtransnet: Hrformer-driven two-modality salient object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Hrtransnet: Hrformer-driven two-modality salient object detection,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.925653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 832f0844-c0b9-468d-909c-c8146cf849b1 · outbound

This paper cites Object segmentation by mining cross-modal semantics,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Object segmentation by mining cross-modal semantics,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.916596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 23a44451-8628-4e07-82c5-732bead5165a · outbound

This paper cites Bag of tricks for image classification with convolutional neural networks,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Bag of tricks for image classification with convolutional neural networks,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.907505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 7da64870-eccf-4a50-be25-cef3bb7c14ac · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-12T04:21:07.753909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T04:21:07.753909Z digest=sha256:63b880b968d17bb4601edb265ade2bdb1fc6a86721d22f547ba700e5cadf13a2

Observation 96462359-9ff8-41b9-abdf-f8e0bf23406f · outbound

This paper cites Hrformer: High-resolution vision transformer for dense predict,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Hrformer: High-resolution vision transformer for dense predict,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.892537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 84dcaa59-8940-4244-ab7a-b86c622f7d8e · outbound

This paper cites Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.885052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.758916Z digest=sha256:c1c454cc6f783c1e23194e59d5595aa92b3fe3be7cf93ce90b26d1500815dd57

Observation ad36a6ef-7210-4fbc-8e04-58dcd1e99fba · outbound

This paper cites Structure-measure: A new way to evaluate foreground maps,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Structure-measure: A new way to evaluate foreground maps,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.876557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.761924Z digest=sha256:4e9518be58c465b2d01aae7304b0bcc03dc30d2f84e5e762fe8228191067605f

Observation 089037f8-bdda-4f46-92aa-5aed07d374ec · outbound

This paper cites Frequency-tuned salient region detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Frequency-tuned salient region detection,

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.868256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.765188Z digest=sha256:96e16f771aaf3dcb58863f7eddaad5ac88c4f66a7c52c9bacf6b88e79fe10f12

Observation 7d31f87d-23f4-442e-978b-8712767fb313 · outbound

This paper cites How to evaluate foreground maps,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection How to evaluate foreground maps,

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.857038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.768585Z digest=sha256:2e2b163431a111ef0f3649a959567fd00f2dc58d4822ffc5d728b34228734a32

Observation 730ee93a-3008-4d79-a516-7b9c579538c8 · outbound

This paper cites Enhanced- alignment measure for binary foreground map evaluation,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Enhanced- alignment measure for binary foreground map evaluation,

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.847581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.772341Z digest=sha256:6f7f2665093288fc240b25a4cfb75d70621415fa105bc32e1a33d58b96078940

Observation f5c95e13-5641-460c-8b91-8e033ff5955c · outbound

This paper cites Saliency filters: Contrast based filtering for salient region detection,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Saliency filters: Contrast based filtering for salient region detection,

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.838923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.775836Z digest=sha256:fece187a9c8ac610086a3b2907f4da8ad8ddd647a24b96f60f048d7d7350cec8

Observation d612d1d4-02fc-4ba1-9779-f822da9ed046 · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Imagenet large scale visual recognition challenge,

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.830630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c78813a2-e857-4979-babd-737d268730b1 · outbound

This paper cites Pyramid scene parsing network,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Pyramid scene parsing network,

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T04:21:07.820616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-12T04:21:07.780724Z digest=sha256:6fe3b2d86665d2173741d06f97e7001d0b0d08d3fb88b6673418d0a11aaaa548

Observation 1b893678-9149-4ec9-b88b-e4d6ca1babaf · outbound

This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

Divide-and-Conquer: Confluent Triple-Flow Network for RGB-T Salient Object Detection Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-12T04:21:07.783291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.