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}
}
This repository contains a PyTorch implementation of "AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis".

AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis | Project Page | Paper | PyTorch implementation for the paper "AD-NeRF: Audio

551 Dec 29, 2022
Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"

The Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more" Arxiv preprint Louay Hazami   ·   Rayhane Mama   ·   Ragavan Thurairatn

Rayhane Mama 144 Dec 23, 2022
TICC is a python solver for efficiently segmenting and clustering a multivariate time series

TICC TICC is a python solver for efficiently segmenting and clustering a multivariate time series. It takes as input a T-by-n data matrix, a regulariz

406 Dec 12, 2022
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models

Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor

Li Yang 1.1k Dec 19, 2022
A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for ONNX.

sam4onnx A very simple tool to rewrite parameters such as attributes and constants for OPs in ONNX models. Simple Attribute and Constant Modifier for

Katsuya Hyodo 6 May 15, 2022
PyTorch code for Composing Partial Differential Equations with Physics-Aware Neural Networks

FInite volume Neural Network (FINN) This repository contains the PyTorch code for models, training, and testing, and Python code for data generation t

Cognitive Modeling 20 Dec 18, 2022
Artificial Intelligence search algorithm base on Pacman

Pacman Search Artificial Intelligence search algorithm base on Pacman Source The Pacman Projects by the University of California, Berkeley. Layouts Di

Day Fundora 6 Nov 17, 2022
Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling"

Official Code Release for "TIP-Adapter: Training-free clIP-Adapter for Better Vision-Language Modeling" Pipeline of Tip-Adapter Tip-Adapter can provid

peng gao 187 Dec 28, 2022
Code for PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Relighting and Material Editing

PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Relighting and Material Editing CVPR 2021. Project page: https://kai-46.github.io/

Kai Zhang 141 Dec 14, 2022
Code and Data for NeurIPS2021 Paper "A Dataset for Answering Time-Sensitive Questions"

Time-Sensitive-QA The repo contains the dataset and code for NeurIPS2021 (dataset track) paper Time-Sensitive Question Answering dataset. The dataset

wenhu chen 35 Nov 14, 2022
Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures.

NLP_0-project Group project for MFIN7036. Our goal is to predict firm profitability with text-based competition measures1. We are a "democratic" and c

3 Mar 16, 2022
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

ALBERT ***************New March 28, 2020 *************** Add a colab tutorial to run fine-tuning for GLUE datasets. ***************New January 7, 2020

Google Research 3k Jan 01, 2023
High dimensional black-box optimizer using Latent Action Monte Carlo Tree Search algorithm

LA-MCTS The code is based of paper Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. Component LA-MCTS has thr

Meta Research 18 Oct 24, 2022
[ICLR2021oral] Rethinking Architecture Selection in Differentiable NAS

DARTS-PT Code accompanying the paper ICLR'2021: Rethinking Architecture Selection in Differentiable NAS Ruochen Wang, Minhao Cheng, Xiangning Chen, Xi

Ruochen Wang 86 Dec 27, 2022
Awesome Graph Classification - A collection of important graph embedding, classification and representation learning papers with implementations.

A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers

Benedek Rozemberczki 4.5k Jan 01, 2023
⚖️🔁🔮🕵️‍♂️🦹🖼️ Code for *Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances* paper.

Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances This repository contains the code for Measuring the Co

Daniel Steinberg 0 Nov 06, 2022
Align and Prompt: Video-and-Language Pre-training with Entity Prompts

ALPRO Align and Prompt: Video-and-Language Pre-training with Entity Prompts [Paper] Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, Steven C.H

Salesforce 127 Dec 21, 2022
Solution of Kaggle competition: Sartorius - Cell Instance Segmentation

Sartorius - Cell Instance Segmentation https://www.kaggle.com/c/sartorius-cell-instance-segmentation Environment setup Build docker image bash .dev_sc

68 Dec 09, 2022
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
Equivariant Imaging: Learning Beyond the Range Space

[Project] Equivariant Imaging: Learning Beyond the Range Space Project about the

Georges Le Bellier 3 Feb 06, 2022