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Paper Citation Record · LEDGER

Seeing Through Fog: Towards Fog-Invariant Action Recognition

As of 16 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2605.20645.

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

pith.paper-citation-record.v1
2605.20645 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:03:42.648937Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

50 of 50 outbound references displayed

  • verified exact7
  • verified fuzzy42
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67951039-fd08-4010-860e-416b8dac01ef · outbound

This paper cites Learning general- ized segmentation for foggy-scenes by bi-directional wavelet guidance.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Learning general- ized segmentation for foggy-scenes by bi-directional wavelet guidance

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:85c4f61b1ce5c9edbadec1289b3d9dcbbe3d7f50d4aeffbe1ea1ee615a1545a7

Observation c2d0c26c-4491-4961-88ed-df4770279aed · outbound

This paper cites Tsnet: deep network for human action recognition in hazy videos.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Tsnet: deep network for human action recognition in hazy videos

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.562067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:7129f6b14989fb294d2f502fc97d4d56d9912b08308422c1593ea86f1debb527

Observation 22d1d61b-f1d2-4f27-8e3f-74cc6976edac · outbound

This paper cites Depth-based end-to-end deep network for human action recognition.IET Computer Vision, 13(1):15–22.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Depth-based end-to-end deep network for human action recognition.IET Computer Vision, 13(1):15–22

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:7eef1c616ef25fd59ae504abe79732ad3eaa19d0c27e4cf7b3f13d824bbb9499

Observation fa61aded-c4f9-4495-9edf-c4bab7545526 · outbound

This paper cites Ost: Refining text knowledge with optimal spatio-temporal descriptor for general video recog- nition.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Ost: Refining text knowledge with optimal spatio-temporal descriptor for general video recog- nition

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.531505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:0ea13fc110051b17d2e45d4298109e30eb5e39acc3f0093579e21f14f8991fca

Observation 4d116884-9a93-4b48-8a8e-35fd59cbcb84 · outbound

This paper cites Prompt-based test-time real image dehazing: a novel pipeline.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Prompt-based test-time real image dehazing: a novel pipeline

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.503136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:a51554047d566badc830d83210df618c91c4ef74f1ebfad40c21f02ef0929362

Observation 39e33153-063c-4619-8aab-80f53f7f5624 · outbound

This paper cites Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention.IEEE Transactions on Image Pro- cessing.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Dea-net: Single image dehazing based on detail-enhanced convolution and content-guided attention.IEEE Transactions on Image Pro- cessing

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.540489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:88f9c672f20ee65c5476efa6083e520cf95d167c6ef75cc6140e0d22d7034fff

Observation 32ebc236-7d31-4e2d-92c1-049064022858 · outbound

This paper cites A real haze video database for haze level evaluation.

Seeing Through Fog: Towards Fog-Invariant Action Recognition A real haze video database for haze level evaluation

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.518787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:67054d6254a068ca0a20d7fc61487ee7c123e7a90b8ff84ca6553d34a36bf99d

Observation d45134d8-87fe-4bcc-9631-01720292b2be · outbound

This paper cites an unresolved cited work.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Unresolved cited work

Reference 8

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:2d00501a4613e976f75ca9461c15f5f5e8cea5c748a6565ae85b7f5ee7d2c48e

Observation a56cdd16-a8d2-4a04-a503-29c3b2b42791 · outbound

This paper cites Rgb- event fusion for robust lane detection.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Rgb- event fusion for robust lane detection

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.565554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:78dc026b9b46a405f1f5fcf195295f0588edd74daf3a6f1e5af7f435ae45ccc7

Observation 5af1866d-1a47-4ead-bd09-20bcac2edab5 · outbound

This paper cites Multi-task learning for video surveillance with limited data.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Multi-task learning for video surveillance with limited data

Reference 10

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Observation 7dd58460-23b9-4ddc-af1d-12ec9768893b · outbound

This paper cites A New Real-World Video Dataset for the Comparison of Defogging Algorithms.

Seeing Through Fog: Towards Fog-Invariant Action Recognition A New Real-World Video Dataset for the Comparison of Defogging Algorithms

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:03:59.200855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:65decd316588e7bc938b9c064c48bb31ec48754d7fb438be097f7dabf2f011b2

Observation d3b38e81-4af3-494a-9cbf-4f82ad7aeb10 · outbound

This paper cites Surveillance face presentation attack detection challenge.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Surveillance face presentation attack detection challenge

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.544184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:740788cece3a659b50440c67c45f855dfdb51c6646f74837fea62432a25e5e42

Observation f6cdac01-e8ec-4706-8144-606e8f8b7650 · outbound

This paper cites Robust object detection in challeng- ing weather conditions.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Robust object detection in challeng- ing weather conditions

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.551527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:e7820cb704e5eff3ca713c3223d7b88490915aa10e5dcc09b61c461a2d8906e0

Observation 44cbc3fb-2882-4b12-ab4c-9e2f13274179 · outbound

This paper cites Populating 3d scenes by learning human-scene interaction.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Populating 3d scenes by learning human-scene interaction

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.536985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:bf13b49095d4b40f3de522d246814c0827dde7bbdae3890fdb48caab5f96ca20

Observation 4580ff47-e187-4fba-acd7-fde27b6c03e8 · outbound

This paper cites Planning-oriented autonomous driving.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Planning-oriented autonomous driving

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.563034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:8cfef0f16154af2e89ec7952241746bea75da94a4e5d8028a95437a5f13f999a

Observation 10814a03-e4da-4be6-a900-47cbae825149 · outbound

This paper cites Hazespace2m: A dataset for haze aware single image dehazing.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Hazespace2m: A dataset for haze aware single image dehazing

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.558626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:d56e448d40e0e7dc62573bf6364fd328422e9b8df35a005408a3c72e8b79fa2a

Observation e3b36bba-e3be-4303-8e93-5fa6c5893b08 · outbound

This paper cites The Kinetics Human Action Video Dataset.

Seeing Through Fog: Towards Fog-Invariant Action Recognition The Kinetics Human Action Video Dataset

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-05-21T06:03:59.185742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:6adebe941b55d6723cb460eccf78f7a5497f7b0de5c11c2570baf62bad19f9da

Observation 57b634c3-a561-49e3-a69b-a9208231a9bf · outbound

This paper cites Leveraging temporal contextualization for video action recognition.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Leveraging temporal contextualization for video action recognition

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.505845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:ae74cb268734903935883ff4fdaf87edaea4242cf2a550039db65ca743ad06cc

Observation 5e6b73a1-ad30-4eed-a717-ef8bf283f8a0 · outbound

This paper cites Hmdb: a large video database for human motion recognition.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Hmdb: a large video database for human motion recognition

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.533101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:30c912df3ce5214dac71e362499b69edccd42320b2c7ba0b0814e4d9e377584e

Observation 02df47e9-9f51-4d62-8db3-fe237dfb0a70 · outbound

This paper cites Benchmarking single- image dehazing and beyond.IEEE Transactions on Image Processing, 28(1):492–505.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Benchmarking single- image dehazing and beyond.IEEE Transactions on Image Processing, 28(1):492–505

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.556028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:542f79bad7959da23666644c4aa825124a4d4f747cca2a34ed0362d6a372b847

Observation 1aeccccb-0041-4903-bb2e-6105a51777b8 · outbound

This paper cites Llama-vid: An image is worth 2 tokens in large language models.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Llama-vid: An image is worth 2 tokens in large language models

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.513144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:03bb510c946f0b2b8d3dbba8835b8d6c88616f18fa672423886ba567e3662bc7

Observation aadf6f29-ade7-4925-8b29-0b1d42644500 · outbound

This paper cites A lightweight multi-level rela- tion network for few-shot action recognition.

Seeing Through Fog: Towards Fog-Invariant Action Recognition A lightweight multi-level rela- tion network for few-shot action recognition

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.554975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:6e4cd2bca588e89667f251b6f165ae36cf1d3ef00e22f940ca3fea569c1a2d3a

Observation 4134401a-d6fc-4440-ba43-bf8695b4e3aa · outbound

This paper cites Storyboard guided Alignment for Fine-grained Video Action Recognition.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Storyboard guided Alignment for Fine-grained Video Action Recognition

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:03:59.197764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:dbd1e9be59d9b3f467ebb1c133d57b1453fd8289c68224f84962b597176e3bad

Observation 64caa8a7-4c26-4b66-bd0b-ba41fc654744 · outbound

This paper cites Narasimhan and Shree K.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Narasimhan and Shree K

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.557770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:43afe1217cd290adf38025dcb4fc75556c2ef933b4b48acb09d4bff798b6b9ce

Observation c2682f2d-15cf-4cd0-98ec-7c327004e928 · outbound

This paper cites Expanding language-image pretrained models for gen- eral video recognition.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Expanding language-image pretrained models for gen- eral video recognition

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.563713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:45696fdba29142417b464cbd7cd4acde4f7ffc80d684a82b7018f080f8f4187b

Observation fc81f3c0-7534-445c-a21f-7f59eb6cd293 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Representation Learning with Contrastive Predictive Coding

Reference 26

Resolution
verified exact
local_arxiv, observed 2026-05-21T06:03:59.188892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:dc43b8f07fadecd0375427b31b8b51a49179057823991bfb167ba79d70c253a1

Observation deba3799-9ca9-4d24-8a60-bd74f0263213 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Seeing Through Fog: Towards Fog-Invariant Action Recognition DINOv2: Learning Robust Visual Features without Supervision

Reference 27

Resolution
verified exact
local_arxiv, observed 2026-05-21T06:03:59.203648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:f2f2be68db91ed53d3dfaa6ef7e14a962ab0f1923a58f140c19c2f19b26f77a2

Observation 4859bfee-cec9-4904-9d2a-aef84cab880e · outbound

This paper cites Bringing a blurry frame alive at high frame-rate with an event camera.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Bringing a blurry frame alive at high frame-rate with an event camera

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.564736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:5789b3279d66132a26f712334f1109e7182392f8f4b36d02b3e5f3f4290c2fc6

Observation 33e65dbe-23b1-4fbc-b80c-aed52335e0a0 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Learning transferable visual models from natural language supervi- sion

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.536710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:ea44495b1d65fdc4fbe23e6ae9a98d068da82cf7c5b078b6ea642b76e8e33aca

Observation 4d766c97-7081-45f5-94ce-dc9db2ffeb77 · outbound

This paper cites Fine-tuned clip models are efficient video learners.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Fine-tuned clip models are efficient video learners

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.553142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:26c8f1529173121bddec5e7b21b8a1de679622cace1854c06cde64e62e279630

Observation f98fb2a6-f22f-4fe9-9646-6bd085a8d66c · outbound

This paper cites Model adaptation with synthetic and real data for semantic dense foggy scene understanding.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Model adaptation with synthetic and real data for semantic dense foggy scene understanding

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.559557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:235be9361f9b2ac58b826ceaebf65b8b71ef031af532f8015eaf71eb27b5c011

Observation fd2d403b-17d9-46c1-89c9-ee7c4a42a83e · outbound

This paper cites Seman- tic foggy scene understanding with synthetic data.Interna- tional Journal of Computer Vision, 126:973–992.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Seman- tic foggy scene understanding with synthetic data.Interna- tional Journal of Computer Vision, 126:973–992

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.516128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:79d48f6db5a6ceb741068e88089ec6d4c6aa606e9adf15b4782d968852a4bb66

Observation 9a0fa117-64ec-4b36-b33c-b1998e78d25e · outbound

This paper cites Acdc: The adverse condi- tions dataset with correspondences for robust semantic driv- ing scene perception.arXiv e-prints, pages arXiv–2104.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Acdc: The adverse condi- tions dataset with correspondences for robust semantic driv- ing scene perception.arXiv e-prints, pages arXiv–2104

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.540849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:40db60b27e54bff85b0a8c4d6dfda5396da1e1250f0f57d5baaca5f2e154c27b

Observation ae923aca-836f-4c79-ad0c-eff0e32009af · outbound

This paper cites A dataset of 101 human action classes from videos in the wild.Center for Research in Computer Vision, 2(11):1–7.

Seeing Through Fog: Towards Fog-Invariant Action Recognition A dataset of 101 human action classes from videos in the wild.Center for Research in Computer Vision, 2(11):1–7

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.547028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:732e86e4494c652ac4934d99a7b5b2192bdaeba52e4faea90c5378afbd41ab46

Observation 4c9b9e08-537c-4180-b85e-a755a07e9a66 · outbound

This paper cites Action recognition in haze using an efficient fusion of spatial and temporal features.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Action recognition in haze using an efficient fusion of spatial and temporal features

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.535176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:39e457ca382e58603e6ffb97dafdda2bb55cf09fc21afe081e61164aeadb225f

Observation d0c286ab-9049-463d-8cdc-099288354131 · outbound

This paper cites Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.Advances in neural information processing systems, 35:10078–10093.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training.Advances in neural information processing systems, 35:10078–10093

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.542819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:25ebc7d81c587f2ce0cef716de46a7a2abee28d73087063261bb814fe5125755

Observation 34699874-826a-459e-8dc4-dc9f24cf3295 · outbound

This paper cites Light-dehazenet: a novel lightweight cnn architecture for single image dehazing.IEEE transactions on image processing, 30:8968–8982.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Light-dehazenet: a novel lightweight cnn architecture for single image dehazing.IEEE transactions on image processing, 30:8968–8982

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.515133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:777b4c2604b342f94e5d2dd3e657a9e825a4d5294b39132c4ce01dd07c07f7b4

Observation ed680049-fdf4-4f79-8df8-e8b8d26c433b · outbound

This paper cites Actionclip: Adapting language-image pretrained models for video action recognition.IEEE Trans- actions on Neural Networks and Learning Systems.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Actionclip: Adapting language-image pretrained models for video action recognition.IEEE Trans- actions on Neural Networks and Learning Systems

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.522660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:20e184e3194251408fca66691db1442eecbaefd9cd4ccf462bf5119db8854cf5

Observation 1b76d017-f14a-406a-baf7-c61918b4b416 · outbound

This paper cites A multimodal, multi-task adapting frame- work for video action recognition.

Seeing Through Fog: Towards Fog-Invariant Action Recognition A multimodal, multi-task adapting frame- work for video action recognition

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.556833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:8f98e133a2fab5f694beb340dadfc23dacdb78e4c10585844c4279cef2539cfc

Observation 6523f70d-f003-45d5-912e-f1ef570f263e · outbound

This paper cites Ucl-dehaze: Towards real-world image dehazing via unsupervised contrastive learning.IEEE Transactions on Im- age Processing.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Ucl-dehaze: Towards real-world image dehazing via unsupervised contrastive learning.IEEE Transactions on Im- age Processing

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.542384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:5b62468579487efcd6e8daa217e30db0cafc62b9eec3afbf6d0b567d6f75286b

Observation 55c74bb3-f861-455c-8833-8d0f92381618 · outbound

This paper cites Vita-clip: Video and text adaptive clip via multimodal prompting.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Vita-clip: Video and text adaptive clip via multimodal prompting

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.560381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:e63f7415e0cf70df2fabd14dec7f240aab1f9efc8161b2c4f4d6477dcefbea34

Observation 951a007a-39bf-4f96-83c2-71c1904a7943 · outbound

This paper cites Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-05-21T06:03:59.191922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:d16ea66ca7be8640b14fb8099f02da2896a31a27d04594a9412b0e1424224849

Observation ed65a019-381f-4bdd-8064-eece049f8e44 · outbound

This paper cites What can simple arithmetic op- erations do for temporal modeling? InProceedings of the IEEE/CVF international conference on computer vision, pages 13712–13722.

Seeing Through Fog: Towards Fog-Invariant Action Recognition What can simple arithmetic op- erations do for temporal modeling? InProceedings of the IEEE/CVF international conference on computer vision, pages 13712–13722

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.554053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:6738c9a02fe66c9b4f007a6e50ae814103ae7978bbb359c79c1515fa61b4e300

Observation 4d2fa56a-60eb-4fc6-91b6-13713ba88ad7 · outbound

This paper cites Bidirectional cross- modal knowledge exploration for video recognition with pre-trained vision-language models.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Bidirectional cross- modal knowledge exploration for video recognition with pre-trained vision-language models

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.546041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:64bdb6fa0284d98a3e7f01af1935d034f544044ad0a12eda44ad92fec0a16e13

Observation 435917c8-3d9b-4000-b864-524a9adcbac3 · outbound

This paper cites Video dehazing via a multi-range temporal alignment network with physical prior.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Video dehazing via a multi-range temporal alignment network with physical prior

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.548832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:c0bf5623d85d3c1547f303e41edffc98a902fa2ed72c0b9b6ef7a2374ebbc13b

Observation 5c66ff12-8c8d-42b0-a39d-3a0b99be1c67 · outbound

This paper cites Language- driven all-in-one adverse weather removal.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Language- driven all-in-one adverse weather removal

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.561389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:fce6c00ad5ed2be988d0dfbd4fe0dfae0ca273009ae56f7c707a360125a3e83e

Observation 119103ea-5fcf-46b9-94a5-4b662a588ead · outbound

This paper cites AIM: Adapting Image Models for Efficient Video Action Recognition.

Seeing Through Fog: Towards Fog-Invariant Action Recognition AIM: Adapting Image Models for Efficient Video Action Recognition

Reference 47

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:03:59.194902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:5ea9be1204e0685a78c15754ca06a6bc320259a60c798a61dac0233458fb685a

Observation 605782a9-ff4e-41be-aa49-85055bae893b · outbound

This paper cites Dehaze- mamba: large multi-modal model guided single image de- hazing via mamba.Visual Intelligence, 3(1):11.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Dehaze- mamba: large multi-modal model guided single image de- hazing via mamba.Visual Intelligence, 3(1):11

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.547997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:21720d4b4a0173cf48b199e1e7cae7b7ed15bb81d057b3b3de95e2a661ce67b3

Observation 6c34deaf-8b1d-463a-9e1f-2c5caa03ad8d · outbound

This paper cites Learning to restore hazy video: A new real-world dataset and a new method.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Learning to restore hazy video: A new real-world dataset and a new method

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.517887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:67556a796032e7ea7ad219dcab6cb8b11228eaa957b955d44857bcbf9e9b0c20

Observation 9bb4f279-8199-49a5-814f-630f91d6ed7e · outbound

This paper cites Dehazing evaluation: Real-world benchmark datasets, criteria, and baselines.IEEE Transactions on Image Processing, 29:6947–6962.

Seeing Through Fog: Towards Fog-Invariant Action Recognition Dehazing evaluation: Real-world benchmark datasets, criteria, and baselines.IEEE Transactions on Image Processing, 29:6947–6962

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-05-21T06:03:59.521603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T06:03:42.648937Z digest=sha256:a973086008f7d65e95f99da9399575e01c257207c7d625607aeac71dd1f34b02

Pith citing papers

No inbound Pith citation observations are available.