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}
}
Cleaned up code for DSTC 10: SIMMC 2.0 track: subtask 2: multimodal coreference resolution

UNITER-Based Situated Coreference Resolution with Rich Multimodal Input: arXiv MMCoref_cleaned Code for the MMCoref task of the SIMMC 2.0 dataset. Pre

Yichen (William) Huang 2 Dec 05, 2022
An implementation of the BADGE batch active learning algorithm.

Batch Active learning by Diverse Gradient Embeddings (BADGE) An implementation of the BADGE batch active learning algorithm. Details are provided in o

125 Dec 24, 2022
Overview of architecture and implementation of TEDS-Net, as described in MICCAI 2021: "TEDS-Net: Enforcing Diffeomorphisms in Spatial Transformers to Guarantee TopologyPreservation in Segmentations"

TEDS-Net Overview of architecture and implementation of TEDS-Net, as described in MICCAI 2021: "TEDS-Net: Enforcing Diffeomorphisms in Spatial Transfo

Madeleine K Wyburd 14 Jan 04, 2023
Pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering".

TRAnsformer Routing Networks (TRAR) This is an official implementation for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visu

Ren Tianhe 49 Nov 10, 2022
A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

faceswap-GAN Adding Adversarial loss and perceptual loss (VGGface) to deepfakes'(reddit user) auto-encoder architecture. Updates Date Update 2018-08-2

3.2k Dec 30, 2022
MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

MVGCN MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. Developer: Fu Hait

13 Dec 01, 2022
This repository contains a toolkit for collecting, labeling and tracking object keypoints

This repository contains a toolkit for collecting, labeling and tracking object keypoints. Object keypoints are semantic points in an object's coordinate frame.

ETHZ ASL 13 Dec 12, 2022
Subpopulation detection in high-dimensional single-cell data

PhenoGraph for Python3 PhenoGraph is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") repr

Dana Pe'er Lab 42 Sep 05, 2022
FAST-RIR: FAST NEURAL DIFFUSE ROOM IMPULSE RESPONSE GENERATOR

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

Anton Jeran Ratnarajah 89 Dec 22, 2022
Using machine learning to predict and analyze high and low reader engagement for New York Times articles posted to Facebook.

How The New York Times can increase Engagement on Facebook Using machine learning to understand characteristics of news content that garners "high" Fa

Jessica Miles 0 Sep 16, 2021
Unofficial implementation of Fast-SCNN: Fast Semantic Segmentation Network

Fast-SCNN: Fast Semantic Segmentation Network Unofficial implementation of the model architecture of Fast-SCNN. Real-time Semantic Segmentation and mo

Philip Popien 69 Aug 11, 2022
This's an implementation of deepmind Visual Interaction Networks paper using pytorch

Visual-Interaction-Networks An implementation of Deepmind visual interaction networks in Pytorch. Introduction For the purpose of understanding the ch

Mahmoud Gamal Salem 166 Dec 06, 2022
Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit

STORM Stochastic Tensor Optimization for Robot Motion - A GPU Robot Motion Toolkit [Install Instructions] [Paper] [Website] This package contains code

NVIDIA Research Projects 101 Dec 12, 2022
Awesome Long-Tailed Learning

Awesome Long-Tailed Learning This repo pays specially attention to the long-tailed distribution, where labels follow a long-tailed or power-law distri

Stomach_ache 284 Jan 06, 2023
ObsPy: A Python Toolbox for seismology/seismological observatories.

ObsPy is an open-source project dedicated to provide a Python framework for processing seismological data. It provides parsers for common file formats

ObsPy 979 Jan 07, 2023
The codes reproduce the figures and statistics in the paper, "Controlling for multiple covariates," by Mark Tygert.

The accompanying codes reproduce all figures and statistics presented in "Controlling for multiple covariates" by Mark Tygert. This repository also pr

Meta Research 1 Dec 02, 2021
Semi-supervised learning for object detection

Source code for STAC: A Simple Semi-Supervised Learning Framework for Object Detection STAC is a simple yet effective SSL framework for visual object

Google Research 348 Dec 25, 2022
Implementation of H-UCRL Algorithm

Implementation of H-UCRL Algorithm This repository is an implementation of the H-UCRL algorithm introduced in Curi, S., Berkenkamp, F., & Krause, A. (

Sebastian Curi 25 May 20, 2022
PyTorch implementation of Densely Connected Time Delay Neural Network

Densely Connected Time Delay Neural Network PyTorch implementation of Densely Connected Time Delay Neural Network (D-TDNN) in our paper "Densely Conne

Ya-Qi Yu 64 Oct 11, 2022
Elegy is a framework-agnostic Trainer interface for the Jax ecosystem.

Elegy Elegy is a framework-agnostic Trainer interface for the Jax ecosystem. Main Features Easy-to-use: Elegy provides a Keras-like high-level API tha

435 Dec 30, 2022