总结的几篇较好论文实现代码（附源代码下载）

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作者：Edison_G

这个是”计算机视觉研究院“新推出的模块，后期我们会陆续为大家带来最新文章及技术的代码实现分享！

  • 《Towards Layer-wise Image Vectorization》(CVPR 2022) 

GitHub: github.com/ma-xu/LIVE

Installation

We suggest users to use the conda for creating new python environment.

Requirement: 5.0<GCC<6.0; nvcc >10.0.

代码语言：javascript复制
git clone git@github.com:ma-xu/LIVE.gitcd LIVEconda create -n live python=3.7conda activate liveconda install -y pytorch torchvision -c pytorchconda install -y numpy scikit-imageconda install -y -c anaconda cmakeconda install -y -c conda-forge ffmpegpip install svgwrite svgpathtools cssutils numba torch-tools scikit-fmm easydict visdompip install opencv-python==4.5.4.60  # please install this version to avoid segmentation fault.cd DiffVGgit submodule update --init --recursivepython setup.py installcd ..

Run Experiments

代码语言：javascript复制
conda activate livecd LIVE# Please modify the paramters accordingly.python main.py --config <config.yaml> --experiment <experiment-setting> --signature <given-folder-name> --target <input-image> --log_dir <log-dir># Here is an simple example:python main.py --config config/base.yaml --experiment experiment_5x1 --signature smile --target figures/smile.png --log_dir log/
  • 《Multimodal Token Fusion for Vision Transformers》(CVPR 2022) 

GitHub: github.com/yikaiw/TokenFusion

  • 《PointAugmenting: Cross-Modal Augmentation for 3D Object  Detection》(CVPR 2022) 

GitHub: github.com/VISION-SJTU/PointAugmenting

  • 《Fantastic questions and where to find them: FairytaleQA -- An authentic dataset for narrative comprehension.》(ACL 2022)

GitHub: github.com/uci-soe/FairytaleQAData

  • 《LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks》(AAAI 2022)

GitHub: github.com/agoodge/LUNAR

Firstly, extract data.zip

To replicate the results on the HRSS dataset with neighbour count k = 100 and "Mixed" negative sampling scheme

  • Extract saved_models.zip
  • Run:
代码语言：javascript复制
python3 main.py --dataset HRSS --samples MIXED --k 100

To train a new model:

代码语言：javascript复制
python3 main.py --dataset HRSS --samples MIXED --k 100 --train_new_model
  • 《Pseudo-Label Transfer from Frame-Level to Note-Level in a Teacher-Student Framework for Singing Transcription from Polyphonic Music》(ICASSP 2022) 

GitHub: github.com/keums/icassp2022-vocal-transcription

  • 《Robust Disentangled Variational Speech Representation Learning for Zero-shot Voice Conversion》(ICASSP 2022) 

GitHub: github.com/jlian2/Robust-Voice-Style-Transfer

Demo：https://jlian2.github.io/Robust-Voice-Style-Transfer/

  • 《HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers》(ICRA 2022) 

GitHub: github.com/NVlabs/handover-sim

代码语言：javascript复制
2022-06-03 16:13:46: Running evaluation for results/2022-02-28_08-57-34_yang-icra2021_s0_test2022-06-03 16:13:47: Evaluation results:|  success rate   |    mean accum time (s)    |                    failure (%)                     ||      (%)        |  exec  |  plan  |  total  |  hand contact   |   object drop   |    timeout     ||:---------------:|:------:|:------:|:-------:|:---------------:|:---------------:|:--------------:|| 64.58 ( 93/144) | 4.864  | 0.036  |  4.900  | 17.36 ( 25/144) | 11.81 ( 17/144) | 6.25 (  9/144) |2022-06-03 16:13:47: Printing scene ids2022-06-03 16:13:47: Success (93 scenes):---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  0    1    2    3    4    5    6    7    8    9   10   12   13   15   16   17   18   19   21   22 23   25   26   27   28   30   33   34   35   36   37   38   42   43   46   49   50   53   54   56 59   60   62   63   64   66   68   69   70   71   72   77   81   83   85   87   89   91   92   93 94   95   96   98  103  106  107  108  109  110  111  112  113  114  115  116  117  120  121  123125  126  127  128  130  131  132  133  137  138  139  141  143---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---2022-06-03 16:13:47: Failure - hand contact (25 scenes):---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  --- 11   14   20   29   39   40   41   44   45   47   51   55   57   58   65   67   74   80   82   88102  105  118  124  136---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---2022-06-03 16:13:47: Failure - object drop (17 scenes):---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  --- 24   31   32   52   61   78   79   84   86   97  101  104  119  122  134  140  142---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---  ---2022-06-03 16:13:47: Failure - timeout (9 scenes):---  ---  ---  ---  ---  ---  ---  ---  --- 48   73   75   76   90   99  100  129  135---  ---  ---  ---  ---  ---  ---  ---  ---2022-06-03 16:13:47: Evaluation complete.
  • 《CDLM: Cross-Document Language Modeling》(EMNLP 2021) 

GitHub: github.com/aviclu/CDLM

You can either pretrain by yourself or use the pretrained CDLM model weights and tokenizer files, which are available on HuggingFace.

Then, use：

代码语言：javascript复制
from transformers import AutoTokenizer, AutoModel# load model and tokenizertokenizer = AutoTokenizer.from_pretrained('biu-nlp/cdlm')model = AutoModel.from_pretrained('biu-nlp/cdlm')
  • 《Continual Learning for Task-Oriented Dialogue Systems》(EMNLP 2021) 

GitHub: github.com/andreamad8/ToDCL

  • 《Torsional Diffusion for Molecular Conformer Generation》(2022)

GitHub: github.com/gcorso/torsional-diffusion

  • 《MMChat: Multi-Modal Chat Dataset on Social Media》(2022) 

GitHub: github.com/silverriver/MMChat

  • 《Can CNNs Be More Robust Than Transformers?》(2022) 

GitHub: github.com/UCSC-VLAA/RobustCNN

  • 《Revealing Single Frame Bias for Video-and-Language Learning》(2022) 

GitHub: github.com/jayleicn/singularity

  • 《Progressive Distillation for Fast Sampling of Diffusion Models》(2022) 

GitHub: github.com/Hramchenko/diffusion_distiller

  • 《Neural Basis Models for Interpretability》(2022) 

GitHub: github.com/facebookresearch/nbm-spam

  • 《Scalable Interpretability via Polynomials》(2022) 

GitHub: github.com/facebookresearch/nbm-spam

  • 《Infinite Recommendation Networks: A Data-Centric Approach》(2022) 

GitHub: github.com/noveens/infinite_ae_cf

  • 《The GatedTabTransformer. An enhanced deep learning architecture for tabular modeling》(2022) 

GitHub: github.com/radi-cho/GatedTabTransformer

Usage：

代码语言：javascript复制
import torchimport torch.nn as nnfrom gated_tab_transformer import GatedTabTransformer
model = GatedTabTransformer(    categories = (10, 5, 6, 5, 8),      # tuple containing the number of unique values within each category    num_continuous = 10,                # number of continuous values    transformer_dim = 32,               # dimension, paper set at 32    dim_out = 1,                        # binary prediction, but could be anything    transformer_depth = 6,              # depth, paper recommended 6    transformer_heads = 8,              # heads, paper recommends 8    attn_dropout = 0.1,                 # post-attention dropout    ff_dropout = 0.1,                   # feed forward dropout    mlp_act = nn.LeakyReLU(0),          # activation for final mlp, defaults to relu, but could be anything else (selu, etc.)    mlp_depth=4,                        # mlp hidden layers depth    mlp_dimension=32,                   # dimension of mlp layers    gmlp_enabled=True                   # gmlp or standard mlp)
x_categ = torch.randint(0, 5, (1, 5))   # category values, from 0 - max number of categories, in the order as passed into the constructor abovex_cont = torch.randn(1, 10)             # assume continuous values are already normalized individually
pred = model(x_categ, x_cont)print(pred)
  • 《Distract Your Attention: Multi-head Cross Attention Network for Facial Expression Recognition》(2022) 

GitHub: github.com/yaoing/DAN

  • 《Towards Principled Disentanglement for Domain Generalization》(2021)

GitHub: github.com/hlzhang109/DDG

  • 《SoundStream: An End-to-End Neural Audio Codec》(2021)

GitHub: github.com/wesbz/SoundStream

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