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编号论文简称标题会议笔记链接
0笔记模板笔记模板梦想A会笔记模板
1GrabCut"GrabCut" — Interactive Foreground Extraction using Iterated Graph CutsSIGGRAPHGrabCut
2EDSREnhanced Deep Residual Networks for Single Image Super-ResolutionCVPREDSR
3TransformerAttention is all you needNeurIPSTransformer
4Vision TransformerAN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALEICLRVision Transformer
5DDPMDenoising Diffusion Probabilistic ModelsNeurIPSDDPM
6CLIPLearning Transferable Visual Models From Natural Language SupervisionICMLCLIP
7Depth ProSHARP MONOCULAR METRIC DEPTH IN LESS THAN A SECONDICLRDepth Pro
8AIMv2Multimodal Autoregressive Pre-training of Large Vision EncodersCVPR2025AIMv2
9DiffUIRSelective Hourglass Mapping for Universal Image Restoration Based on Diffusion ModelCVPR2024DiffUIR
10OSEDiffOne-Step Effective Diffusion Network for Real-World Image Super-ResolutionNeurIPS2024OSEDiff
11S3DiffDegradation-Guided One-Step Image Super-Resolution with Diffusion PriorsCVPR2025S3Diff
12MoCE-IRComplexity Experts are Task-Discriminative Learners for Any Image RestorationCVPR2025MoCE-IR
13RestorMixerAn efficient and mixed heterogeneous model for image restorationArXivRestorMixer
14MoPEMoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal FusionArXivMoPE
15GIFNetOne Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image FusionCVPR2025GIFNet
16DT4AIOUIRLearning Dual Transformers for All-In-One Image Restoration from a Frequency PerspectiveArxivDT4AIOUIR
17DEnetINTERPRETABLE UNSUPERVISED JOINT DENOISING AND ENHANCEMENT FOR REAL-WORLD LOW-LIGHT SCENARIOSICLR 2025DEnet
18PFT-SRProgressive Focused Transformer for Single Image Super-ResolutionCVPR 2025PFT-SR
19TEAFormerEnhancing Image Restoration Transformer via Adaptive Translation EquivarianceICCV 2025TEAFormer
20Converse2DReverse Convolution and Its Applications to Image RestorationICCV 2025Converse2D
21HINTDevil is in the Uniformity: Exploring Diverse Learners within Transformer for Image RestorationICCV 2025HINT
22LLIE_SurveyLow-Light Image and Video Enhancement Using Deep Learning: A SurveyTPAMI 2021LLIE_Survey
23CLIP_LITLow-Light Image and Video Enhancement Using Deep Learning: A SurveyICCV 2023CLIP_LIT

最后更新时间:2025-10-10 20:10:58

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