Research - MCL - Media Communications Lab.

Research

MCL Research – Semantic Lines and Photographic Composition

Posted on 2020년 8월 7일

Posted in Research

MCL Research – Frame-Rate Up-Conversion

Posted on 2020년 8월 7일

Posted in Research

MCL Research – Single-Image Depth Estimation

Posted on 2020년 8월 7일

Posted in Featured, Research

MCL Research – Image Enhancement

Posted on 2020년 8월 3일

Posted in Research

MCL Research – Image Enhancement

Posted on 2020년 8월 3일

Posted in Featured, Research

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Recent Comments
seonholee on [Weekly] RAFT: Recurrent All-Pairs Field Transforms for Optical Flow
junheumpark on Papers 12 & 13
seonholee on [Weekly] BubbleNets Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames
seonholee on [Weekly] Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition
HanUl Kim on No lectures on 12 September, 14 September
Recent Board

[Weekly] SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow

[Weekly] Attention-aware Learning for Hyperparameter Prediction in Image Processing Pipelines

[Weekly] LabelDistill: Label-guided Cross-modal Knowledge Distillation for Camera-based 3D Object Detection

[Weekly] Local Distortion Aware Efficient Transformer Adaptation for Image Quality Assessment

[Weekly] MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation

Recent Posts

[2024] Two JVCIR papers

[ECCV 2024] Lane detection and face obfuscation

[2024] 대학원생 모집

[CVPR 2024] Depths, Lines, Quality, and Segmentation

[ICLR 2024] Unsupervised order learning