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miaomiao1989's Projects

adversarial icon adversarial

Code and hyperparameters for the paper "Generative Adversarial Networks"

bold icon bold

BOLD - Binary Online Learned Descriptor

caffe-segnet icon caffe-segnet

Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling

carc icon carc

Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval

cf_tracking icon cf_tracking

C++ Implementation of two correlation filter based visual trackers

clm-framework icon clm-framework

CLM-framework (a.k.a Cambridge Face Tracker) is a framework for various Constrained Local Model based face tracking and landmark detection algorithms and their extensions/applications. Includes CLM-Z and CLNF.

cnn-face-point-detection icon cnn-face-point-detection

This project is about the utilization of CNN to detect human face point. Trained with the dataset LFW and images from Internet.

coco icon coco

MS COCO API - http://mscoco.org/

convnet-torch icon convnet-torch

Training Deep Convolutional Networks on visual classification tasks

crfasrnn icon crfasrnn

This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/

cvg_ardrone2_ibvs icon cvg_ardrone2_ibvs

We present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following objects from a multi-rotor UAV, without any dependence on GPS systems. Our proposed method tracks a user specified object continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people, occlusions and also fast moving objects; showing the robustness of the proposed systems against perturbations and illumination changes. Our experiments show that the system is able to track a great variety of objects present in suburban areas, among others: people, windows, AC machines, cars and plants. urls: http://www.vision4uav.eu/?q=following and http://robotics.asu.edu/ardrone2_ibvs/ .

cvpr15-cfss icon cvpr15-cfss

Full version (training+testing) of implementation of Shizhan Zhu et al.'s CVPR-15 work Face Alignment by Coarse-to-Fine Shape Searching

data-science-ipython-notebooks icon data-science-ipython-notebooks

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, Spark, Hadoop MapReduce, HDFS, matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. https://bit.ly/data-notes

dcgan.torch icon dcgan.torch

A torch implementation of http://arxiv.org/abs/1511.06434

deep-goggle icon deep-goggle

Source code for "Understanding Deep Image Representations by Inverting Them", CVPR 2015

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