dlut-dimt Goto Github PK

a-rotation-robust-shape-transformer-for-cartoon-character-recognition

code

dpe-deep-prior-ensemble

The source code of paper “Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement”

ellipse-detector

ellipse-detector-with-characteristic-mapping

fkda

Source code for our IEEE TPAMI paper: Yi Wang, Yi Ding, Xiangjian He, Xin Fan*, Chi Lin, Fengqi Li, Tianzhu Wang, Zhongxuan Luo, Jiebo Luo, “Novelty Detection and Online Learning for Chunk Data Streams”, IEEE TPAMI, 2019. (DOI: 10.1109/TPAMI.2020.2965531)

flda-qr-and-iflda-qr

It is the code of "Fast Online Incremental Learning on Mixture Streaming Data"

hcnccode

Source codes of "Hierarchical Projective Invariant Contexts for Shape Recognition"

leetcode

Play Leetcode with different Programming language

linematching

Line matching code of ECCV2016

multipropreg

podm

A Bridging Framework for Model Optimization and Deep Propagation (NIPS-2018)

poster

realworld-underwater-image-enhancement-ruie-benchmark

Paper “Real-world Underwater Enhancement: Challenging, Benchmark and Efficient Solutions” https://arxiv.org/abs/1901.05320

reconet

ECCV 2022 | Recurrent Correction Network for Fast and Efficient Multi-modality Image Fusion.

ruod

sample-of-dut-multi-view

20 samples from DUT Multi view dataset

segmar

shape-to-gradient-regression

An implementation of Shape-to-gradient regression in "Explicit Shape Regression with Characteristic Number for Facial Landmark Localization "

tardal

CVPR 2022 | Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection.

tgdof

# TGDOF This is the testing code of TGDOF for CS-MRI. Running the script "AddPath" and then the "Demo_TGDOF" to test the basic deep framework for CS-MRI. TestData ------------ The testing MR slices used in experiments, including 25 T1-weighted data and 25 T2-weighted data. The slices are extracted from the subset of the IXI datasets: http://brain-development.org/ixi-dataset/ ArtifactsModel ------------ The pre-trained model used in Module \mathcal{N}. SamplingPatter: ------------ The three kinds of sampling patterns at five different sampling ratios (10% to 50%). If you utilize this code, please cite the related paper: <br> @inproceedings{liu2019theoretically,<br> title={A theoretically guaranteed deep optimization framework for robust compressive sensing mri},<br> author={Liu, Risheng and Zhang, Yuxi and Cheng, Shichao and Fan, Xin and Luo, Zhongxuan},<br> booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},<br> volume={33},<br> pages={4368--4375},<br> year={2019} }

tmm-3d-face-reconstruction

Dual Neural Networks Coupling Data Regression with Explicit Priors for Monocular 3D Face Reconstruction

tvcj

Experiment results

two-layer-gpr-dehazing

The source code of Two-layer Gaussian Process Regression with Example Selection for Image Dehazing, TCSVT

two-layer-gpr-dehazing-pg

underwater-image-enhancement-algorithms