The code is an implementation of Feedback Convolutional Neural Network for Visual Localization and Segmentation.

Overview

Feedback Convolutional Neural Network for Visual Localization and Segmentation

The code is an implementation of Feedback Convolutional Neural Network for Visual Localization and Segmentation. The code is written in PyTorch, very simple to understand.

There is also a Caffe implementation, please check it if you use Caffe and Matlab.

Requirement:

  • Python 3
  • Pytorch 0.4.0

How to run:

open the ipython notebooks with jupyter notebook

then open vgg_fr.ipynb or vgg_fsp.ipynb, these are the two main files for demonstrate feedback idea.

How it looks:

If you run vgg_fsp.ipynb without modification of code, you are supposed to see below visualization:

Input image:

Image gradient with respect to the target label:

Image gradient with respect to the target label after 4 iterations of feedback selective pruning (FSP):

Files explanation:

  • vgg_fr.ipynb: the main file that defines the vgg feedback network with the feedback recovering mechanism and run a feedback visualization on examplar images.
  • vgg_fsp.ipynb: the main file that defines the vgg feedback network with the feedback selective pruning mechanism and run a feedback visualization on examplar images.
  • images: storing exmaplar images
  • imagenet1000_clsid_to_human.txt: storing image net 1000 class names, for visualization and understanding purpose
  • test/simple_test.ipynb: unit test for a simple feedback network, using a simple fully connected structure
  • test/vgg_test.ipynb: unit test for the loading of a pretrained vgg network, then check the weights copying from pretrained network to a new defined network interface

Citation

Please consider citing in your publications if it helps your research:

@inproceedings{cao2015look,
  title={Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks},
  author={Cao, Chunshui and Liu, Xianming and Yang, Yi and Yu, Yinan and Wang, Jiang and Wang, Zilei and Huang, Yongzhen and Wang, Liang and Huang, Chang and Xu, Wei and others},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={2956--2964},
  year={2015}
}
CVPR 2021 Official Pytorch Code for UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

UC2 UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training Mingyang Zhou, Luowei Zhou, Shuohang Wang, Yu Cheng, Linjie Li, Zhou Yu,

Mingyang Zhou 28 Dec 30, 2022
Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad to your characters in Modo.

Applicator Kit for Modo Applicator Kit for Modo allow you to apply Apple ARKit Face Tracking data from your iPhone or iPad with a TrueDepth camera to

Andrew Buttigieg 3 Aug 24, 2021
Convert human motion from video to .bvh

video_to_bvh Convert human motion from video to .bvh with Google Colab Usage 1. Open video_to_bvh.ipynb in Google Colab Go to https://colab.research.g

Dene 306 Dec 10, 2022
Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters.

Joint Unsupervised Learning (JULE) of Deep Representations and Image Clusters. Overview This project is a Torch implementation for our CVPR 2016 paper

Jianwei Yang 278 Dec 25, 2022
Repo for CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST in Tensorflow 2 Code for the paper: "CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning" by Chen Wei, Ki

Google Research 75 Nov 01, 2022
Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Hurdles to Progress in Long-form Question Answering This repository contains the official scripts and datasets accompanying our NAACL 2021 paper, "Hur

Kalpesh Krishna 41 Nov 08, 2022
Personals scripts using ageitgey/face_recognition

HOW TO USE pip3 install requirements.txt Add some pictures of known people in the folder 'people' : a) Create a folder called by the name of the perso

Antoine Bollengier 1 Jan 06, 2022
Model Agnostic Interpretability for Multiple Instance Learning

MIL Model Agnostic Interpretability This repo contains the code for "Model Agnostic Interpretability for Multiple Instance Learning". Overview Executa

Joe Early 10 Dec 17, 2022
A more easy-to-use implementation of KPConv

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 35 Dec 14, 2022
Pytorch Implementation of Adversarial Deep Network Embedding for Cross-Network Node Classification

Pytorch Implementation of Adversarial Deep Network Embedding for Cross-Network Node Classification (ACDNE) This is a pytorch implementation of the Adv

陈志豪 8 Oct 13, 2022
Official repository for "Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems"

Action-Based Conversations Dataset (ABCD) This respository contains the code and data for ABCD (Chen et al., 2021) Introduction Whereas existing goal-

ASAPP Research 49 Oct 09, 2022
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.

English | 简体中文 | 繁體中文 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 🤗 Transformers provides thousands of pretrained mo

Hugging Face 77.2k Jan 02, 2023
Direct application of DALLE-2 to video synthesis, using factored space-time Unet and Transformers

DALLE2 Video (wip) ** only to be built after DALLE2 image is done and replicated, and the importance of the prior network is validated ** Direct appli

Phil Wang 105 May 15, 2022
Keyword-BERT: Keyword-Attentive Deep Semantic Matching

project discription An implementation of the Keyword-BERT model mentioned in my paper Keyword-Attentive Deep Semantic Matching (Plz cite this github r

1 Nov 14, 2021
Dungeons and Dragons randomized content generator

Component based Dungeons and Dragons generator Supports Entity/Monster Generation NPC Generation Weapon Generation Encounter Generation Environment Ge

Zac 3 Dec 04, 2021
Pytorch implementation for DFN: Distributed Feedback Network for Single-Image Deraining.

DFN:Distributed Feedback Network for Single-Image Deraining Abstract Recently, deep convolutional neural networks have achieved great success for sing

6 Nov 05, 2022
A Peer-to-peer Platform for Secure, Privacy-preserving, Decentralized Data Science

PyGrid is a peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft. PyGrid is also the central serv

OpenMined 615 Jan 03, 2023
TyXe: Pyro-based BNNs for Pytorch users

TyXe: Pyro-based BNNs for Pytorch users TyXe aims to simplify the process of turning Pytorch neural networks into Bayesian neural networks by leveragi

87 Jan 03, 2023
Si Adek Keras is software VR dangerous object detection.

Si Adek Python Keras Sistem Informasi Deteksi Benda Berbahaya Keras Python. Version 1.0 Developed by Ananda Rauf Maududi. Developed date: 24 November

Ananda Rauf 1 Dec 21, 2021
This repository is an implementation of paper : Improving the Training of Graph Neural Networks with Consistency Regularization

CRGNN Paper : Improving the Training of Graph Neural Networks with Consistency Regularization Environments Implementing environment: GeForce RTX™ 3090

THUDM 28 Dec 09, 2022