31 skills found · Page 1 of 2
twtygqyy / Pytorch VdsrVDSR (CVPR2016) pytorch implementation
reedscot / Cvpr2016Learning Deep Representations of Fine-grained Visual Descriptions
bertinetto / Staple[CVPR'16] Staple: Complementary Learners for Real-Time Tracking"
zeakey / DeepSkeletonCode for CVPR2016 and TIP papers about Object Skeleton Detection with Fully Convolutional Neural Networks.
yg33717 / DSH Tensorflowimplemement of DEEP SUPERVISED HASHING FOR FAST IMAGE RETRIEVAL_CVPR2016
gyglim / Video2gif DatasetThe Video2GIF dataset with 100k GIFs from our paper at CVPR2016
geliuhao / CVPR2016 HandPoseEstimationRobust 3D Hand Pose Estimation in Single Depth Images: from Single-View CNN to Multi-View CNNs
gyglim / Video2gif CodeVideo2GIF neural network model from our paper at CVPR2016
makarandtapaswi / MovieQA CVPR2016Contains approaches introduced in the MovieQA benchmark dataset paper
enomotokenji / Pytorch Neural Style TransferUnofficial pytorch implementation of Image Style Transfer Using Convolutional Neural Networks [Gatys+, CVPR2016]
bobbens / Cvpr2016 StylenetCode for our CVPR 2016 paper on Fashion styles in 128 floats.
shaoxiaohu / CVPR2016Paper about face detection, landmark detection, recognition, reconstruction, text detection and so on.
MingtaoGuo / Style Transfer With Neural AlgorithmImplementation of style transfer by tensorflow, for detail please see the paper "Image Style Transfer Using Convolutional Neural Networks"(CVPR2016)
byangderek / CRAFTCodes for CVPR2016 paper "CRAFT Objects from Images"
fireblue204 / Social LSTM复现CVPR2016李飞飞团队提出的轨迹预测网络social-LSTM
Chinmayrane16 / ReconNet PyTorchA non-iterative algorithm to reconstruct images from compressively sensed measurements.
umariqb / 3D Pose Estimation CVPR2016No description available
uestcchicken / Crowd Counting MCNNA very ugly unofficial implementation of CVPR2016 paper Single-Image Crowd Counting via Multi Column Convolutional Neural Network using tensorflow and keras
zhengxiawu / Scrap Cvpr2016No description available
jalayrac / InstructionVideosCode for the paper "Unsupervised Learning from Narrated Instruction Videos", CVPR2016
