From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning | Paper Citation Record

Observation ed78185a-c165-49c4-ad3e-0aee5b94d893 · outbound

This paper cites Handbook of image and video processing.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Handbook of image and video processing

Reference 1

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Observation ff56f457-1d40-4c78-8db8-abf57aa51d92 · outbound

This paper cites Retinexformer: One-stage retinex- based transformer for low-light image enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Retinexformer: One-stage retinex- based transformer for low-light image enhancement

Reference 2

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This paper cites The cityscapes dataset for semantic urban scene understanding.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning The cityscapes dataset for semantic urban scene understanding

Reference 3

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This paper cites Multitask aet with orthogonal tangent regularity for dark object detection.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Multitask aet with orthogonal tangent regularity for dark object detection

Reference 4

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This paper cites You only need 90k parameters to adapt light: a light weight trans- former for image enhancement and exposure correction.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning You only need 90k parameters to adapt light: a light weight trans- former for image enhancement and exposure correction

Reference 5

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This paper cites Boosting object detection with zero-shot day-night domain adaptation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Boosting object detection with zero-shot day-night domain adaptation

Reference 6

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This paper cites Yolov3: An incremental improvement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Yolov3: An incremental improvement

Reference 7

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Observation ee1a8dbd-a083-4573-80f1-105d6bb086ce · outbound

This paper cites Learning a simple low-light image enhancer from paired low-light instances.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Learning a simple low-light image enhancer from paired low-light instances

Reference 8

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This paper cites Scene point cloud understanding and recon- struction technologies in 3d space.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Scene point cloud understanding and recon- struction technologies in 3d space

Reference 9

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This paper cites Digital image process- ing.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Digital image process- ing

Reference 10

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Observation 79292c32-5b52-45d4-8e38-c966ababb6c5 · outbound

This paper cites Zero-reference deep curve estimation for low-light image enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Zero-reference deep curve estimation for low-light image enhancement

Reference 11

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This paper cites Lime: Low-light im- age enhancement via illumination map estimation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Lime: Low-light im- age enhancement via illumination map estimation

Reference 12

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This paper cites R2rnet: Low-light image enhancement via real-low to real-normal network.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning R2rnet: Low-light image enhancement via real-low to real-normal network

Reference 13

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This paper cites Deep residual learning for image recognition.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Deep residual learning for image recognition

Reference 14

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This paper cites Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation

Reference 15

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This paper cites Hrda: Context-aware high-resolution domain-adaptive semantic segmentation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Hrda: Context-aware high-resolution domain-adaptive semantic segmentation

Reference 16

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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This paper cites Lora: Low-rank adaptation of large language models.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Lora: Low-rank adaptation of large language models

Reference 18

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This paper cites LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models

Reference 19

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This paper cites Enlightengan: Deep light enhancement without paired supervision.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Enlightengan: Deep light enhancement without paired supervision

Reference 20

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This paper cites Ensembling off-the-shelf models for gan training.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Ensembling off-the-shelf models for gan training

Reference 21

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This paper cites The retinex theory of color vision.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning The retinex theory of color vision

Reference 22

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Observation 92e19c23-f8cc-48dc-9a63-c9f0445106b5 · outbound

This paper cites Zero-shot day-night domain adaptation with a physics prior.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Zero-shot day-night domain adaptation with a physics prior

Reference 23

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This paper cites Learning to enhance low-light image via zero-reference deep curve esti- mation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Learning to enhance low-light image via zero-reference deep curve esti- mation

Reference 24

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This paper cites Dsfd: dual shot face detector.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Dsfd: dual shot face detector

Reference 25

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This paper cites Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 26

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This paper cites Light the night: A multi-condition diffusion framework for unpaired low-light enhancement in autonomous driving.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Light the night: A multi-condition diffusion framework for unpaired low-light enhancement in autonomous driving

Reference 27

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This paper cites Iterative prompt learning for unsupervised backlit image enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Iterative prompt learning for unsupervised backlit image enhancement

Reference 28

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This paper cites Refinenet: Multi-path refinement networks for high- resolution semantic segmentation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Refinenet: Multi-path refinement networks for high- resolution semantic segmentation

Reference 29

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This paper cites Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement

Reference 30

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This paper cites Getting to know low- light images with the exclusively dark dataset.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Getting to know low- light images with the exclusively dark dataset

Reference 31

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This paper cites Decoupled weight de- cay regularization.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Decoupled weight de- cay regularization

Reference 32

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This paper cites Similarity min-max: Zero-shot day-night domain adap- tation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Similarity min-max: Zero-shot day-night domain adap- tation

Reference 33

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This paper cites Taming diffusion model for exemplar-based image translation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Taming diffusion model for exemplar-based image translation

Reference 34

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This paper cites Toward fast, flexible, and robust low-light image enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Toward fast, flexible, and robust low-light image enhancement

Reference 35

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From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Histogram-based transformation function estimation for low-light image enhancement

Reference 36

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This paper cites One-Step Image Translation with Text-to-Image Models.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning One-Step Image Translation with Text-to-Image Models

Reference 37

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This paper cites Portraitbooth: A versatile portrait model for fast identity-preserved personalization.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Portraitbooth: A versatile portrait model for fast identity-preserved personalization

Reference 38

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This paper cites Learning transferable visual models from natural language supervision.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Learning transferable visual models from natural language supervision

Reference 39

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

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 40

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From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning High-resolution image synthesis with latent diffusion models

Reference 41

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From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning High-resolution image synthesis with latent diffusion models

Reference 42

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This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 43

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From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Photorealistic text-to-image diffusion models with deep language understanding

Reference 44

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This paper cites Guided curriculum model adaptation and uncertainty-aware evalua- tion for semantic nighttime image segmentation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Guided curriculum model adaptation and uncertainty-aware evalua- tion for semantic nighttime image segmentation

Reference 45

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This paper cites Adversarial diffusion distillation, 2023.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Adversarial diffusion distillation, 2023

Reference 46

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This paper cites Adversarial diffusion distillation.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Adversarial diffusion distillation

Reference 47

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This paper cites Zero-ig: Zero-shot illumination-guided joint denoising and adaptive enhancement for low-light images.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Zero-ig: Zero-shot illumination-guided joint denoising and adaptive enhancement for low-light images

Reference 48

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From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Unresolved cited work

Reference 49

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Observation f96dcad8-9a5b-4565-8954-7adfe0535242 · outbound

This paper cites Hla-face: Joint high-low adaptation for low light face detection.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Hla-face: Joint high-low adaptation for low light face detection

Reference 50

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Observation eca69bb6-3af2-47ed-93a3-06c661aea60d · outbound

This paper cites Noise4denoise: Leveraging noise for unsupervised point cloud denoising.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Noise4denoise: Leveraging noise for unsupervised point cloud denoising

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.357387Z

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Observation 5227c165-e3a8-4cdb-b880-fb2924c2e903 · outbound

This paper cites Unsupervised illumination adaptation for low-light vi- sion.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Unsupervised illumination adaptation for low-light vi- sion

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.325869Z

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Observation c184899c-8863-47ad-8f29-1cb21a736d24 · outbound

This paper cites Zero-reference low-light enhancement via physical quadru- ple priors.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Zero-reference low-light enhancement via physical quadru- ple priors

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.300406Z

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Observation 0fcf5f3c-12fd-4fe8-adba-c674d20190b9 · outbound

This paper cites Deep retinex decomposition for low-light enhancement,.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Deep retinex decomposition for low-light enhancement,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.273182Z

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Observation e642b21c-a6d0-45f7-89d5-763756cee1b1 · outbound

This paper cites Learning semantic-aware knowledge guidance for low-light image enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Learning semantic-aware knowledge guidance for low-light image enhancement

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.244798Z

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Observation 27c6b132-c1aa-4379-86ae-7f73caebe576 · outbound

This paper cites Boosting night-time scene pars- ing with learnable frequency.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Boosting night-time scene pars- ing with learnable frequency

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.225594Z

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Observation da2da897-6f2d-43f7-a3f3-83d3a56f2a7a · outbound

This paper cites Pig: Prompt images guidance for night-time scene parsing.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Pig: Prompt images guidance for night-time scene parsing

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.203046Z

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Observation 8ae4f28b-d905-4732-b0d5-82485d8daefa · outbound

This paper cites HVI: A New Color Space for Low-light Image Enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning HVI: A New Color Space for Low-light Image Enhancement

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T18:26:24.539899Z

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Observation 3dbab701-7450-4e07-9b3d-1003e75a8f2f · outbound

This paper cites Learnable differencing center for night- time depth perception.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Learnable differencing center for night- time depth perception

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.168309Z

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Observation 73be2bfb-3051-4462-95e1-2402254c89af · outbound

This paper cites Wider face: A face detection benchmark.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Wider face: A face detection benchmark

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.144836Z

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Observation 32fa7444-9802-41ce-b613-ebbc1a6e0243 · outbound

This paper cites Implicit neural representation for coopera- tive low-light image enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Implicit neural representation for coopera- tive low-light image enhancement

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.124737Z

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Observation 9c9cf896-1408-40a9-bedb-adb2afc9dacf · outbound

This paper cites Scheirer, Zhangyang Wang, , and et al.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Scheirer, Zhangyang Wang, , and et al

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.086519Z

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Observation c341d6d3-79bb-4b76-b07b-8c559ae47362 · outbound

This paper cites Sparse gradient regularized deep retinex network for robust low-light image enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Sparse gradient regularized deep retinex network for robust low-light image enhancement

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.063401Z

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Observation 7f497dda-3713-4573-a9c1-065fa433984e · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T18:26:24.569956Z

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Observation 6d4ef6e4-793b-4350-8e82-2e48bf9a4669 · outbound

This paper cites Bdd100k: A diverse driving dataset for heterogeneous multitask learning.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Bdd100k: A diverse driving dataset for heterogeneous multitask learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.040673Z

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Observation a253c0c5-bd47-4a81-8cc7-dcec96205ac5 · outbound

This paper cites Dual illu- mination estimation for robust exposure correction.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Dual illu- mination estimation for robust exposure correction

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.021073Z

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Observation 92a28324-b283-4dea-9068-5ec320912b39 · outbound

This paper cites Kindling the darkness: A practical low-light image enhancer.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Kindling the darkness: A practical low-light image enhancer

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:25.000089Z

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Observation ec1df232-9dbe-461c-93b3-a55748d7c190 · outbound

This paper cites Dsd-matchingnet: Deformable sparse-to-dense feature matching for learning accurate correspondences.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Dsd-matchingnet: Deformable sparse-to-dense feature matching for learning accurate correspondences

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:24.979343Z

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Observation 5ddd0f03-0675-4d7c-b566-20bfad7b1c7f · outbound

This paper cites Learning semantic degradation-aware guidance for recognition-driven unsupervised low-light im- age enhancement.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Learning semantic degradation-aware guidance for recognition-driven unsupervised low-light im- age enhancement

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:26:24.956818Z

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Observation d0288d9c-d77c-452b-9554-16237127fd02 · outbound

This paper cites Unpaired image-to-image translation using cycle- consistent adversarial networks.

From Enhancement to Understanding: Build a Generalized Bridge for Low-light Vision via Semantically Consistent Unsupervised Fine-tuning Unpaired image-to-image translation using cycle- consistent adversarial networks

Reference 70

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T18:26:24.797902Z

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