Official Pytorch Implementation of: "Semantic Diversity Learning for Zero-Shot Multi-label Classification"(2021) paper

Overview

Semantic Diversity Learning for Zero-Shot Multi-label Classification

Paper

Official PyTorch Implementation

Avi Ben-Cohen, Nadav Zamir, Emanuel Ben Baruch, Itamar Friedman, Lihi Zelnik-Manor
DAMO Academy, Alibaba Group

Abstract

Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray numerous semantically diverse labels. As challenging as this task is, it is an essential task to tackle since it represents many real-world cases, such as image retrieval of natural images. We argue that using a single embedding vector to represent an image, as commonly practiced, is not sufficient to rank both relevant seen and unseen labels accurately. This study introduces an end-to-end model training for multi-label zero-shot learning that supports semantic diversity of the images and labels. We propose to use an embedding matrix having principal embedding vectors trained using a tailored loss function. In addition, during training, we suggest up-weighting in the loss function image samples presenting higher semantic diversity to encourage the diversity of the embedding matrix. Extensive experiments show that our proposed method improves the zero-shot model’s quality in tag-based image retrieval achieving SoTA results on several common datasets (NUS-Wide, COCO, Open Images).

Semantic Diversity Learning (SDL) Implementation

In this PyTorch file, we provide an implementation of our semantic diversity learning (SDL) loss for zero shot multi-label classification.

Pretrained Models

We provide a pre-trained model on NUS-WIDE dataset, which can be found here

Inference Code

We provide an inference code, that demonstrates how to load our model, pre-process an image and do actuall inference. Example run:

python infer.py  \
--model_path=./models_local/NUS_mtresnet_224.pth \
--model_name=tresnet_m \
--pic_path=./pics/140016_215548610_422b79b4d7_m.jpg \
--top_k=10 \

which will result in:

Note that predicted "unseen" tags are indicated by * tag-name *.

Training Code

Training code will be available soon.

Citation

@misc{bencohen2021semantic,
      title={Semantic Diversity Learning for Zero-Shot Multi-label Classification}, 
      author={Avi Ben-Cohen and Nadav Zamir and Emanuel Ben Baruch and Itamar Friedman and Lihi Zelnik-Manor},
      year={2021},
      eprint={2105.05926},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgements

Several images from NUS-WIDE dataset are used in this project.

Some components of this code implementation are adapted from the repository https://github.com/Alibaba-MIIL/ASL.

We would like to thank Tal Ridnik for his valuable comments and suggestions.

[CVPR2021 Oral] End-to-End Video Instance Segmentation with Transformers

VisTR: End-to-End Video Instance Segmentation with Transformers This is the official implementation of the VisTR paper: Installation We provide instru

Yuqing Wang 687 Jan 07, 2023
Styled text-to-drawing synthesis method. Featured at the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design

Styled text-to-drawing synthesis method. Featured at the 2021 NeurIPS Workshop on Machine Learning for Creativity and Design

Peter Schaldenbrand 247 Dec 23, 2022
McGill Physics Hackathon 2021: Reaction-Diffusion Models for the Generation of Biological Patterns

DiffuseAnimals: Reaction-Diffusion Models for the Generation of Biological Patterns Introduction Reaction-diffusion equations can be utilized in order

Austin Szuminsky 2 Mar 07, 2022
naked is a Python tool which allows you to strip a model and only keep what matters for making predictions.

naked is a Python tool which allows you to strip a model and only keep what matters for making predictions. The result is a pure Python function with no third-party dependencies that you can simply c

Max Halford 24 Dec 20, 2022
This is the official implement of paper "ActionCLIP: A New Paradigm for Action Recognition"

This is an official pytorch implementation of ActionCLIP: A New Paradigm for Video Action Recognition [arXiv] Overview Content Prerequisites Data Prep

268 Jan 09, 2023
2D Human Pose estimation using transformers. Implementation in Pytorch

PE-former: Pose Estimation Transformer Vision transformer architectures perform very well for image classification tasks. Efforts to solve more challe

Panteleris Paschalis 23 Oct 17, 2022
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation (NeurIPS2021 Benchmark and Dataset Track)

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Kingdrone 174 Dec 22, 2022
Compressed Video Action Recognition

Compressed Video Action Recognition Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl. In CVPR, 2018. [Proj

Chao-Yuan Wu 479 Dec 26, 2022
Genetic feature selection module for scikit-learn

sklearn-genetic Genetic feature selection module for scikit-learn Genetic algorithms mimic the process of natural selection to search for optimal valu

Manuel Calzolari 260 Dec 14, 2022
An introduction to bioimage analysis - http://bioimagebook.github.io

Introduction to Bioimage Analysis This book tries explain the main ideas of image analysis in a practical and engaging way. It's written primarily for

Bioimage Book 20 Nov 28, 2022
End-to-End Speech Processing Toolkit

ESPnet: end-to-end speech processing toolkit system/pytorch ver. 1.3.1 1.4.0 1.5.1 1.6.0 1.7.1 1.8.1 1.9.0 ubuntu20/python3.9/pip ubuntu20/python3.8/p

ESPnet 5.9k Jan 04, 2023
This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper

DeepShift This is project is the implementation of the DeepShift: Towards Multiplication-Less Neural Networks paper, that aims to replace multiplicati

Mostafa Elhoushi 88 Dec 23, 2022
An implementation of the paper "A Neural Algorithm of Artistic Style"

A Neural Algorithm of Artistic Style implementation - Neural Style Transfer This is an implementation of the research paper "A Neural Algorithm of Art

Srijarko Roy 27 Sep 20, 2022
Block Sparse movement pruning

Movement Pruning: Adaptive Sparsity by Fine-Tuning Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; ho

Hugging Face 54 Dec 20, 2022
Survival analysis (SA) is a well-known statistical technique for the study of temporal events.

DAGSurv Survival analysis (SA) is a well-known statistical technique for the study of temporal events. In SA, time-to-an-event data is modeled using a

Rahul Kukreja 1 Sep 05, 2022
Official Implementation of "Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras"

Multi Camera Pig Tracking Official Implementation of Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras CVPR2021 CV4Animals Workshop P

44 Jan 06, 2023
This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation)

This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation) Usage example python dynamic_inverted_softmax.py --sims_train

36 Dec 29, 2022
ICON: Implicit Clothed humans Obtained from Normals

ICON: Implicit Clothed humans Obtained from Normals arXiv, December 2021. Yuliang Xiu · Jinlong Yang · Dimitrios Tzionas · Michael J. Black Table of C

Yuliang Xiu 1.1k Dec 30, 2022
FaceAnon - Anonymize people in images and videos using yolov5-crowdhuman

Face Anonymizer Blur faces from image and video files in /input/ folder. Require

22 Nov 03, 2022
PyTorch implementation of Neural Dual Contouring.

NDC PyTorch implementation of Neural Dual Contouring. Citation We are still writing the paper while adding more improvements and applications. If you

Zhiqin Chen 140 Dec 26, 2022