HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation | Paper Citation Record

Observation 3d315389-8702-4c77-9388-645b1695509c · outbound

This paper cites an unresolved cited work.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-15T13:05:39.009167Z

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Observation 003866d2-a215-4f3d-9bf3-bcc554923069 · outbound

This paper cites Unsupervised labeled lane markers using maps.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Unsupervised labeled lane markers using maps

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.010997Z

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Observation b46bd7ab-7f80-4639-85e7-a82607cb8ee8 · outbound

This paper cites Non-local im- age dehazing.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Non-local im- age dehazing

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.016838Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 381d7bbc-d3f0-4239-a68b-dca83f1c83a8 · outbound

This paper cites ALL snow removed: Single image desnowing algorithm using hi- erarchical dual-tree complex wavelet representation and con- tradict channel loss.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation ALL snow removed: Single image desnowing algorithm using hi- erarchical dual-tree complex wavelet representation and con- tradict channel loss

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.057932Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 04ddcb94-3bef-4882-90d5-7c7ca25b8a19 · outbound

This paper cites Bidirectional multi-scale implicit neural representations for image derain- ing.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Bidirectional multi-scale implicit neural representations for image derain- ing

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.007401Z

Source-reported events for the cited work

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Observation e27b2797-7ffd-4d9c-b7fe-42bda1f9070b · outbound

This paper cites Comfyui: The most powerful and mod- ular stable diffusion gui with a graph/nodes interface.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Comfyui: The most powerful and mod- ular stable diffusion gui with a graph/nodes interface

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.003564Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b54311c7-4489-4517-94a2-53a5d1c664d4 · outbound

This paper cites Diffu- sion models beat gans on image synthesis.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Diffu- sion models beat gans on image synthesis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.005384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c595e4b2-27a0-4964-aefc-f19b1eeecb04 · outbound

This paper cites Prompt tuning inversion for text-driven image editing using diffusion models.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Prompt tuning inversion for text-driven image editing using diffusion models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.001588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ed8f5712-da6b-4774-be04-f0a00eaac2fc · outbound

This paper cites Generative adversarial nets.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Generative adversarial nets

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.997325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0e005101-e528-407c-8dfd-ed8fc3d27310 · outbound

This paper cites Effi- cientderain: Learning pixel-wise dilation filtering for high- efficiency single-image deraining.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Effi- cientderain: Learning pixel-wise dilation filtering for high- efficiency single-image deraining

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.984023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a15498d4-e9ba-4a87-b27f-1d0b99781670 · outbound

This paper cites Plug-and-play diffusion features for text-driven image-to-image translation.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Plug-and-play diffusion features for text-driven image-to-image translation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.981738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 56f709ad-b97c-4795-923e-5c954d68798c · outbound

This paper cites Classifier-Free Diffusion Guidance.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Classifier-Free Diffusion Guidance

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-15T13:05:38.664997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6a6f087-c82e-4651-90fe-3074d85fbd57 · outbound

This paper cites Denoising dif- fusion probabilistic models.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Denoising dif- fusion probabilistic models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.991208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b1aa4698-6599-4018-968d-1efc0394ff58 · outbound

This paper cites Clrernet: Improving con- fidence of lane detection with laneiou.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Clrernet: Improving con- fidence of lane detection with laneiou

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.978775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e2dcfddc-cd04-4a21-b62a-2b58c0da18ed · outbound

This paper cites Learning lightweight lane detection cnns by self atten- tion distillation.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Learning lightweight lane detection cnns by self atten- tion distillation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.988762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b5dbc66-8f65-4867-a146-968aea152f71 · outbound

This paper cites Clr- net: Cross layer refinement network for lane detection.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Clr- net: Cross layer refinement network for lane detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.993160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation acdcd594-e28d-431e-98e1-9453cfa13605 · outbound

This paper cites Kingma and Max Welling.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Kingma and Max Welling

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.055899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 68f60fc9-57d3-49c3-8e00-63bb0da48adc · outbound

This paper cites Cond- lanenet: a top-to-down lane detection framework based on conditional convolution.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Cond- lanenet: a top-to-down lane detection framework based on conditional convolution

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.062271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 871c570a-b0cc-4c85-ad1b-19ce10a2d1a2 · outbound

This paper cites an unresolved cited work.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-05-15T13:05:39.054065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ab63024b-04c1-4479-9751-15bdf4f46a25 · outbound

This paper cites Desnownet: Context-aware deep network for snow removal.IEEE Trans.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Desnownet: Context-aware deep network for snow removal.IEEE Trans

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.046594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 70d08baa-14b8-4a74-935f-e6974be512ee · outbound

This paper cites Improved denoising diffusion probabilistic models.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Improved denoising diffusion probabilistic models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.044563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 26ad0b30-8925-4771-899c-44d8dd3d5699 · outbound

This paper cites GLIDE: towards photorealis- tic image generation and editing with text-guided diffusion models.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation GLIDE: towards photorealis- tic image generation and editing with text-guided diffusion models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.048506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 32fa911d-7701-4357-99d5-47f40eb4b155 · outbound

This paper cites Spatial as deep: Spatial cnn for traffic scene understanding.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Spatial as deep: Spatial cnn for traffic scene understanding

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.050562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2822ae39-bedc-4216-99a2-f058b27a7336 · outbound

This paper cites Patil, Sunil Gupta, Santu Rana, Svetha Venkatesh, and Subrahmanyam Murala.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Patil, Sunil Gupta, Santu Rana, Svetha Venkatesh, and Subrahmanyam Murala

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.052302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dd6df1b8-d008-4de9-ba85-f9c998cd4781 · outbound

This paper cites Lane detection and classification using cas- caded cnns.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Lane detection and classification using cas- caded cnns

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.976308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7f557993-1650-4147-9e22-fd8b30379239 · outbound

This paper cites Clrkdnet: Speeding up lane detection with knowledge distillation.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Clrkdnet: Speeding up lane detection with knowledge distillation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.060197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 55d8a30b-57ee-4e7b-9466-d4d677790dce · outbound

This paper cites Weatherdg: Llm-assisted procedural weather generation for domain-generalized semantic segmentation.IEEE Robotics Autom.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Weatherdg: Llm-assisted procedural weather generation for domain-generalized semantic segmentation.IEEE Robotics Autom

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.999577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 716eca15-529b-4a85-8a55-62bf1118a6f8 · outbound

This paper cites an unresolved cited work.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-05-15T13:05:39.038389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d6b639cc-aade-44b0-a45d-11fe641535dc · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Learn- ing transferable visual models from natural language super- vision

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.040680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 00a70e87-86c9-4c6f-9084-1f327c807382 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-05-15T13:05:38.672953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dbf0eee2-e8ee-4aaf-9103-c9b8a860b952 · outbound

This paper cites Gated fusion net- work for single image dehazing.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Gated fusion net- work for single image dehazing

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.034174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1352ed29-591a-4298-9bf4-d2a465aec037 · outbound

This paper cites an unresolved cited work.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-05-15T13:05:39.036280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 069ba3ca-4e98-486a-a67d-0a5e3ef67962 · outbound

This paper cites Variational inference with normalizing flows.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Variational inference with normalizing flows

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.042711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 88b61011-8668-413c-9bc0-135d562618cf · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation High-resolution image syn- thesis with latent diffusion models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.031848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ef4f276f-e788-466d-a4e9-9855f063307b · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Photorealistic text-to-image diffusion models with deep language understanding

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.064291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4f98e006-d635-49ff-a864-6e73b828a1f7 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.027331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c980372e-dc99-4295-80a2-3f0c3c07b3b7 · outbound

This paper cites Fea- ture enhancement based on cyclegan for nighttime vehicle detection.IEEE Access, 9:849–859.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Fea- ture enhancement based on cyclegan for nighttime vehicle detection.IEEE Access, 9:849–859

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.029864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a85d101f-203f-4764-b489-b3698cd75084 · outbound

This paper cites Tabelini, R.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Tabelini, R

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.022931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4bd13171-94a8-4c3e-ab60-14a8ec2f9c2a · outbound

This paper cites Paix˜ao, Claudine Badue, Alberto F.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Paix˜ao, Claudine Badue, Alberto F

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.995249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4af6024e-8be3-48cd-aa69-aacaf48afdaf · outbound

This paper cites Effective data augmentation with diffusion models.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Effective data augmentation with diffusion models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:38.986230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5e410372-321f-4641-af96-2aa904bc8580 · outbound

This paper cites A keypoint-based global association network for lane detection.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation A keypoint-based global association network for lane detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.021017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 268dfc84-fadf-4d64-8506-8e4c44a8755a · outbound

This paper cites Fenet: Focusing en- hanced network for lane detection.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Fenet: Focusing en- hanced network for lane detection

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.025155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 617147bb-e199-46d1-a66f-a355e1366bae · outbound

This paper cites Pretraining is All You Need for Image-to-Image Translation.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Pretraining is All You Need for Image-to-Image Translation

Reference 43

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T13:05:38.669535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 849d94c9-77e4-4777-8fa0-940c6b99622a · outbound

This paper cites Ad- net: Lane shape prediction via anchor decomposition.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Ad- net: Lane shape prediction via anchor decomposition

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.066457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a9839540-0630-49c6-9c24-cd189bbae777 · outbound

This paper cites Resa: Recurrent feature-shift ag- gregator for lane detection.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Resa: Recurrent feature-shift ag- gregator for lane detection

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.018791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c7a5d1c0-209c-488d-811f-002548be68f6 · outbound

This paper cites Learn- ing weather-general and weather-specific features for image restoration under multiple adverse weather conditions.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Learn- ing weather-general and weather-specific features for image restoration under multiple adverse weather conditions

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.013058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f7be8155-0173-47a7-8a6f-4cefb0be840d · outbound

This paper cites Visualization in Real-World In Figure 7, a comparison is presented between the samples generated by our framework and real-world samples.

HG-Lane: High-Fidelity Generation of Lane Scenes under Adverse Weather and Lighting Conditions without Re-annotation Visualization in Real-World In Figure 7, a comparison is presented between the samples generated by our framework and real-world samples

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-15T13:05:39.014995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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