Code of the paper "Shaping Visual Representations with Attributes for Few-Shot Learning (ASL)".

chx_nju

Last update: Sep 1, 2022

Related tags

Deep Learning ASL

Overview

Shaping Visual Representations with Attributes for Few-Shot Learning

This code implements the Shaping Visual Representations with Attributes for Few-Shot Learning (ASL).

Citation

If you find our work useful, please consider citing our work using the bibtex:

@Article{chen2021asl,
	author  = {Chen, Haoxing and Li, Huaxiong and Li, Yaohui and Chen, Chunlin},
	title   = {Shaping Visual Representations with Attributes for Few-Shot Learning},
	journal = {arXiv preprint arXiv:2112.06398},
	year    = {2021},
}

Prerequisites

Linux
Python 3.7
Pytorch 1.2
Torchvision 0.4
GPU + CUDA CuDNN

Datasets

You can download datasets automatically by adding --download when running the program. However, here we give steps to manually download datasets to prevent problems such as poor network connection: CUB:

Create the dir ASL/datasets/cub;
Download CUB_200_2011.tgz from here, and put the archive into ASL/datasets/cub;
Running the program with --download.

SUN:

Create the dir ASL/datasets/sun;
Download the archive of images from here, and put the archive into ASL/datasets/sun;
Download the archive of attributes from here, and put the archive into ASL/datasets/sun;
Running the program with --download.

Few-shot Classification

Download data and run on multiple GPUs with special settings:

python train.py --train-data [train_data] --test-data [test_data] --backbone [backbone] --num-shots [num_shots] --batch-tasks [batch_tasks] --train-tasks [train_tasks] --semantic-type [semantic_type] --multi-gpu --download

Run on CUB dataset, ResNet-12 backbone, 1-shot, single GPU

python train.py --train-data cub --test-data cub --backbone resnet12 --num-shots 1 --batch-tasks 4 --train-tasks 60000 --semantic-type class_attributes

Note that batch tasks are set to 4/1 when training 1-shot/5-shot tasks.

Our code is based on AGAM and TorchMeta.

Contacts

Please feel free to contact us if you have any problems.

Email: [email protected]

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Comments

can't find these two files

@chenhaoxing It is a pleasure to read your paper《Shaping Visual Representations with Attributes for Few-Shot Learning (IEEE SPL)》.but I reported an error for missing documents.I can't find these two files. Could you help me check them？thank you！ filename = 'train_data.hdf5' filename_labels = 'train_labels.json'

opened by zjrzjr666 1
CVE-2007-4559 Patch

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

opened by TrellixVulnTeam 0

Owner

chx_nju

Master student in Nanjing University.

GitHub

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Code of the paper "Shaping Visual Representations with Attributes for Few-Shot Learning (ASL)".

Related tags

Overview

Shaping Visual Representations with Attributes for Few-Shot Learning

Citation

Prerequisites

Datasets

Few-shot Classification

Our code is based on AGAM and TorchMeta.

Contacts

You might also like...

PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

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Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

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Code for the Shortformer model, from the paper by Ofir Press, Noah A. Smith and Mike Lewis.

PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Official code for paper "Optimization for Oriented Object Detection via Representation Invariance Loss".

Code for our CVPR 2021 paper "MetaCam+DSCE"

Code for our CVPR2021 paper coordinate attention

Comments

can't find these two files

CVE-2007-4559 Patch

Patching CVE-2007-4559

Owner

chx_nju

This is the official source code for SLATE. We provide the code for the model, the training code, and a dataset loader for the 3D Shapes dataset. This code is implemented in Pytorch.

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TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)

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Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"

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