• 《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: 5.010.0.

git clone git@github.com:ma-xu/LIVE.gitcd LIVE
conda create -n live python=3.7
conda activate live
conda install -y pytorch torchvision -c pytorch
conda install -y numpy scikit-image
conda install -y -c anaconda cmake
conda install -y -c conda-forge ffmpeg
pip install svgwrite svgpathtools cssutils numba torch-tools scikit-fmm easydict visdom
pip install opencv-python==4.5.4.60  # please install this version to avoid segmentation fault.cd DiffVG
git submodule update --init --recursive
python setup.py installcd ..

Run Experiments

conda activate live
cd LIVE
# Please modify the paramters accordingly.
python main.py --config  --experiment  --signature  --target  --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:

python3 main.py--datasetHRSS--samplesMIXED--k 100

To train a new model:

python3 main.py--datasetHRSS--samplesMIXED--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

2022-06-03 16:13:46: Running evaluation for results/2022-02-28_08-57-34_yang-icra2021_s0_test
2022-06-03 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 ids
2022-06-03 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   2223   25   26   27   28   30   33   34   35   36   37   38   42   43   46   49   50   53   54   5659   60   62   63   64   66   68   69   70   71   72   77   81   83   85   87   89   91   92   9394   95   96   98  103  106  107  108  109  110  111  112  113  114  115  116  117  120  121  123
125  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   88
102  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：

from transformers import AutoTokenizer, AutoModel
# load model and tokenizer
tokenizer = 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：

import torch
import torch.nn as nn
from gated_tab_transformer import GatedTabTransformermodel = GatedTabTransformer(categories = (10, 5, 6, 5, 8),      # tuple containing the number of unique values within each categorynum_continuous = 10,                # number of continuous valuestransformer_dim = 32,               # dimension, paper set at 32dim_out = 1,                        # binary prediction, but could be anythingtransformer_depth = 6,              # depth, paper recommended 6transformer_heads = 8,              # heads, paper recommends 8attn_dropout = 0.1,                 # post-attention dropoutff_dropout = 0.1,                   # feed forward dropoutmlp_act = nn.LeakyReLU(0),          # activation for final mlp, defaults to relu, but could be anything else (selu, etc.)mlp_depth=4,                        # mlp hidden layers depthmlp_dimension=32,                   # dimension of mlp layersgmlp_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 above
x_cont = torch.randn(1, 10)             # assume continuous values are already normalized individuallypred = 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