CaFM-pytorch ICCV ACCEPT Introduction of dataset VSD4K

Overview

CaFM-pytorch ICCV ACCEPT

Introduction of dataset VSD4K

Our dataset VSD4K includes 6 popular categories: game, sport, dance, vlog, interview and city. Each category is consisted of various video length, including: 15s, 30s, 45s, etc. For a specific category and its specific video length, there are 3 scaling factors: x2, x3 and x4. In each file, there are HR images and its corresponding LR images. 1-n are training images , n - (n + n/10) are test images. (we select test image 1 out of 10). The dataset can be obtained from [https://pan.baidu.com/s/14pcsC7taB4VAa3jvyw1kog] (passward:u1qq) and google drive [https://drive.google.com/drive/folders/17fyX-bFc0IUp6LTIfTYU8R5_Ot79WKXC?usp=sharing].

e.g.:game 15s
dataroot_gt: VSD4K/game/game_15s_1/DIV2K_train_HR/00001.png
dataroot_lqx2: VSD4K/game/game_15s_1/DIV2K_train_LR_bicubic/X2/00001_x2.png
dataroot_lqx3: VSD4K/game/game_15s_1/DIV2K_train_LR_bicubic/X3/00001_x3.png
dataroot_lqx4: VSD4K/game/game_15s_1/DIV2K_train_LR_bicubic/X4/00001_x4.png

Proposed method

Introduction

Our paper "Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation" has been submitted to 2021 ICCV. we aim to use super resolution network to improve the quality of video delivery recently. The whole precedure is shown below. We devide the whole video into several chunks and apply a joint training framework with Content aware Feature Module(CaFM) to train each chunk simultaneously. With our method, each video chunk only requires less than 1% of original parameters to be streamed, achieving even better SR performance. We conduct extensive experiments across various SR backbones(espcn,srcnn,vdsr,edsr16,edsr32,rcan), video time length(15s-10min), and scaling factors(x2-x4) to demonstrate the advantages of our method. All pretrain models(15s, 30s, 45s) of game category can be found in this link [https://pan.baidu.com/s/1P18FULL7CIK1FAa2xW56AA] (passward:bjv1) and google drive link [https://drive.google.com/drive/folders/1_N64A75iwgbweDBk7dUUDX0SJffnK5-l?usp=sharing].

Figure 1. The whole procedure of adopting content-aware DNNs for video delivery. A video is first divided into several chunks and the server trains one model for each chunk. Then the server delivers LR video chunks and models to client. The client runs the inference to super-resolve the LR chunks and obtain the SR video.

Quantitative results

We show our quantitative results in the table below. For simplicity, we only demonstrate the results on game and vlog datasets. We compare our method M{1-n} with M0 and S{1-n}. The experiments are conducted on EDSR.

  • M0: a EDSR without CaFM module, train on whole video.
  • Si: a EDSR without a CaFM module, train on one specific chunk i.
  • M{1-n}ours: a EDSR with n CaFM modules, train on n chunks simultaneously.
Dataset Game15s Game30s Game45s
Scale x2 x3 x4 x2 x3 x4 x2 x3 x4
M0 42.24 35.88 33.44 41.84 35.54 33.05 42.11 35.75 33.33
S{1-n} 42.82 36.42 34.00 43.07 36.73 34.17 43.22 36.72 34.32
M{1-n} Ours 43.13 37.04 34.47 43.37 37.12 34.58 43.46 37.31 34.79
Dataset Vlog15s Vlog30s Vlog45s
Scale x2 x3 x4 x2 x3 x4 x2 x3 x4
M0 48.87 44.51 42.58 47.79 43.38 41.24 47.98 43.58 41.53
S{1-n} 49.10 44.80 42.83 48.20 43.68 41.55 48.48 44.12 42.12
M{1-n} Ours 49.30 45.03 43.11 48.55 44.15 42.16 48.61 44.24 42.39

Quatitative results

We show the quatitative results in the figure below.

  • bicubic: SR images are obtained by bicubic
  • H.264/H.265: use the default setting of FFmpeg to generate the H.264 and H.265 videos

Dependencies

  • Python >= 3.6
  • Torch >= 1.0.0
  • opencv-python
  • numpy
  • skimage
  • imageio
  • matplotlib

Quickstart

M0 demotes the model without Cafm module which is trained on the whole dataset. S{1-n} denotes n models that trained on n chunks of video. M{1-n} demotes one model along with n Cafm modules that trained on the whole dataset. M{1-n} is our proposed method.

How to set data_range

n is the total frames in a video. We select one test image out of 10 training images. Thus, in VSD4K, 1-n is its training dataset, n-(n+/10) is the test dataset. Generally, we set 5s as the length of one chunk. Hence, 15s consists 3 chunks, 30s consists 6 chunks, etc.

Video length(train images + test images) chunks M0/M{1-n} S1 S2 S3 S4 S5 S6 S7 S8 S9
15s(450+45) 3 1-450/451-495 1-150/451-465 151-300/466-480 301-450/481-495 - - - - - -
30s(900+95) 6 1-900/901-990 1-150/901-915 151-300/916-930 301-450/931-945 451-600/946-960 601-750/961-975 751-900/976-990 - - -
45s(1350+135) 9 1-1350/1351-1485 1-150/1351-1365 151-300/1366-1380 301-450/1381-1395 451-600/1396-1410 601-750/1411-1425 751-900/1426-1440 901-1050/1441-1455 1051-1200/1456-1470 1201-1350/1471-1485

Train

For simplicity, we only demonstrate how to train 'game_15s' by our method.

  • For M{1-n} model:
CUDA_VISIBLE_DEVICES=3 python main.py --model {EDSR/ESPCN/VDSRR/SRCNN/RCAN} --scale {scale factor} --patch_size {patch size} --save {name of the trained model} --reset --data_train DIV2K --data_test DIV2K --data_range {train_range}/{test_range} --cafm --dir_data {path of data} --use_cafm --batch_size {batch size} --epoch {epoch} --decay {decay} --segnum {numbers of chunk} --length
e.g. 
CUDA_VISIBLE_DEVICES=3 python main.py --model EDSR --scale 2 --patch_size 48 --save trainm1_n --reset --data_train DIV2K --data_test DIV2K --data_range 1-450/451-495 --cafm --dir_data /home/datasets/VSD4K/game/game_15s_1 --use_cafm --batch_size 64 --epoch 500 --decay 300 --segnum 3 --is15s

You can apply our method on your own images. Place your HR images under YOURS/DIV2K_train_HR/, with the name start from 00001.png. Place your corresponding LR images under YOURS/DIV2K_train_LR_bicubic/X2, with the name start from 00001_x2.png.

e.g.:
dataroot_gt: YOURS/DIV2K_train_HR/00001.png
dataroot_lqx2: YOURS/DIV2K_train_LR_bicubic/X2/00001_x2.png
dataroot_lqx3: YOURS/DIV2K_train_LR_bicubic/X3/00001_x3.png
dataroot_lqx4: YOURS/DIV2K_train_LR_bicubic/X4/00001_x4.png
  • The running command is like:
CUDA_VISIBLE_DEVICES=3 python main.py --model {EDSR/ESPCN/VDSRR/SRCNN/RCAN} --scale {scale factor} --patch_size {patch size} --save {name of the trained model} --reset --data_train DIV2K --data_test DIV2K --data_range {train_range}/{test_range} --cafm --dir_data {path of data} --use_cafm --batch_size {batch size} --epoch {epoch} --decay {decay} --segnum {numbers of chunk} --length
  • For example:
e.g. 
CUDA_VISIBLE_DEVICES=3 python main.py --model EDSR --scale 2 --patch_size 48 --save trainm1_n --reset --data_train DIV2K --data_test DIV2K --data_range 1-450/451-495 --cafm --dir_data /home/datasets/VSD4K/game/game_15s_1 --use_cafm --batch_size 64 --epoch 500 --decay 300 --segnum 3 --is15s

Test

For simplicity, we only demonstrate how to run 'game' category of 15s. All pretrain models(15s, 30s, 45s) of game category can be found in this link [https://pan.baidu.com/s/1P18FULL7CIK1FAa2xW56AA] (passward:bjv1) and google drive link [https://drive.google.com/drive/folders/1_N64A75iwgbweDBk7dUUDX0SJffnK5-l?usp=sharing].

  • For M{1-n} model:
CUDA_VISIBLE_DEVICES=3 python main.py --data_test DIV2K --scale {scale factor} --model {EDSR/ESPCN/VDSRR/SRCNN/RCAN} --test_only --pre_train {path to pretrained model} --data_range {train_range} --{is15s/is30s/is45s} --cafm  --dir_data {path of data} --use_cafm --segnum 3
e.g.:
CUDA_VISIBLE_DEVICES=3 python main.py --data_test DIV2K --scale 4 --model EDSR --test_only --pre_train /home/CaFM-pytorch/experiment/edsr_x2_p48_game_15s_1_seg1-3_batch64_k1_g64/model/model_best.pt --data_range 1-150 --is15s --cafm  --dir_data /home/datasets/VSD4K/game/game_15s_1 --use_cafm --segnum 3

Additional

We also demonstrate our method in vimeo dataset and HEVC test sequence. These datasets and all trained models will be released as soon as possible. By the way, we add SEFCNN.py into our backbone list which is suggested by reviewer.The code will be updated regularly.

Acknowledgment

AdaFM proposed a closely related method for continual modulation of restoration levels. While they aimed to handle arbitrary restoration levels between a start and an end level, our goal is to compress the models of different chunks for video delivery. The reader is encouraged to review their work for more details. Please also consider to cite AdaFM if you use the code. [https://github.com/hejingwenhejingwen/AdaFM]

Framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample resolution

Sample-specific Bayesian Networks A framework for estimating the structures and parameters of Bayesian networks (DAGs) at per-sample or per-patient re

Caleb Ellington 1 Sep 23, 2022
paper: Hyperspectral Remote Sensing Image Classification Using Deep Convolutional Capsule Network

DC-CapsNet This is a tensorflow and keras based implementation of DC-CapsNet for HSI in the Remote Sensing Letters R. Lei et al., "Hyperspectral Remot

LEI 7 Nov 29, 2022
This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive Selective Coding)

HCSC: Hierarchical Contrastive Selective Coding This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive

YUANFAN GUO 111 Dec 20, 2022
[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts

Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.

45 Nov 30, 2022
A naive ROS interface for visualDet3D.

YOLO3D ROS Node This repo contains a Monocular 3D detection Ros node. Base on https://github.com/Owen-Liuyuxuan/visualDet3D All parameters are exposed

Yuxuan Liu 19 Oct 08, 2022
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation (ICCV 2021)

A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation (ICCV 2021) This repository contains the official implemen

81 Dec 14, 2022
Contrastive Multi-View Representation Learning on Graphs

Contrastive Multi-View Representation Learning on Graphs This work introduces a self-supervised approach based on contrastive multi-view learning to l

Kaveh 208 Dec 23, 2022
Image inpainting using Gaussian Mixture Models

dmfa_inpainting Source code for: MisConv: Convolutional Neural Networks for Missing Data (to be published at WACV 2022) Estimating conditional density

Marcin Przewięźlikowski 8 Oct 09, 2022
ECCV2020 paper: Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards. Code and Data.

This repo contains some of the codes for the following paper Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards. Code

Xuewen Yang 56 Dec 08, 2022
Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

VQGAN-CLIP-GENERATOR Overview This is a package (with available notebook) for running VQGAN+CLIP locally, with a focus on ease of use, good documentat

Ryan Hamilton 98 Dec 30, 2022
TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition

TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition Xue, Wenyuan, et al. "TGRNet: A Table Graph Reconstruction Network for Ta

Wenyuan 68 Jan 04, 2023
Resilience from Diversity: Population-based approach to harden models against adversarial attacks

Resilience from Diversity: Population-based approach to harden models against adversarial attacks Requirements To install requirements: pip install -r

0 Nov 23, 2021
Emotion classification of online comments based on RNN

emotion_classification Emotion classification of online comments based on RNN, the accuracy of the model in the test set reaches 99% data: Large Movie

1 Nov 23, 2021
Group Activity Recognition with Clustered Spatial Temporal Transformer

GroupFormer Group Activity Recognition with Clustered Spatial-TemporalTransformer Backbone Style Action Acc Activity Acc Config Download Inv3+flow+pos

28 Dec 12, 2022
Code for the paper "There is no Double-Descent in Random Forests"

Code for the paper "There is no Double-Descent in Random Forests" This repository contains the code to run the experiments for our paper called "There

2 Jan 14, 2022
An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine

A Gentle Introduction to Satellite Image Processing Welcome to this introductory course on Satellite Image Analysis! Satellite imagery has become a pr

Edward Oughton 32 Jan 03, 2023
LBK 20 Dec 02, 2022
Test-Time Personalization with a Transformer for Human Pose Estimation, NeurIPS 2021

Transforming Self-Supervision in Test Time for Personalizing Human Pose Estimation This is an official implementation of the NeurIPS 2021 paper: Trans

41 Nov 28, 2022
Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

Flow Flow is a computational framework for deep RL and control experiments for traffic microsimulation. See our website for more information on the ap

867 Jan 02, 2023
LSSY量化交易系统

LSSY量化交易系统 该项目是本人3年来研究量化慢慢积累开发的一套系统,属于早期作品慢慢修改而来,仅供学习研究,回测分析,实盘交易部分未公开

55 Oct 04, 2022