Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)

Overview

Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation (NeurIPS 2021)

The implementation of Reducing Infromation Bottleneck for Weakly Supervised Semantic Segmentation, Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon, NeurIPS 2021. [[paper]]

outline

outline

Abstract

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes an information bottleneck, and as a result, only a subset of the task-relevant information is passed on to the output. We first support this argument through a simulated toy experiment and then propose a method to reduce the information bottleneck by removing the last activation function. In addition, we introduce a new pooling method that further encourages the transmission of information from non-discriminative regions to the classification. Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the PASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation.

Installation

  • We kindly refer to the offical implementation of IRN.

Usage

Step 1. Prepare Dataset

  • Download Pascal VOC dataset here.

  • Download MS COCO images from the official COCO website here.

  • Download semantic segmentation annotations for the MS COCO dataset here.

  • Directory hierarchy

    Dataset
    ├── VOC2012_SEG_AUG       # unzip VOC2012_SEG_AUG.zip           
    ├── coco_2017             # mkdir coco_2017
    │   ├── coco_seg_anno     # included in coco_annotations_semantic.zip
    └── └── JPEGImages        # include train and val images downloaded from the official COCO website

Step 2. Prepare pre-trained classifier

  • Pre-trained model used in this paper: Pascal VOC, MS COCO.
  • You can also train your own classifiers following IRN.

Step 3. Generate and evaluate the pseudo ground-truth masks for PASCAL VOC and MS COCO

  • PASCAL VOC
bash get_pseudo_gt_VOC.sh
  • MS COCO
bash get_pseudo_gt_COCO.sh

Step 4. Train a semantic segmentation network

Acknowledgment

This code is heavily borrowed from IRN, thanks jiwoon-ahn!

Owner
Jungbeom Lee
Jungbeom Lee
5 Jan 05, 2023
Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (CVAMD)

Is it Time to Replace CNNs with Transformers for Medical Images? Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (C

Christos Matsoukas 80 Dec 27, 2022
A library for implementing Decentralized Graph Neural Network algorithms.

decentralized-gnn A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes. De

Multimedia Knowledge and Social Analytics Lab 5 Nov 07, 2022
A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

HOW TO USE THIS PROJECT A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets Based on DeepLabCut toolbox, we run wit

1 Jan 10, 2022
Keras documentation, hosted live at keras.io

Keras.io documentation generator This repository hosts the code used to generate the keras.io website. Generating a local copy of the website pip inst

Keras 2k Jan 08, 2023
Weak-supervised Visual Geo-localization via Attention-based Knowledge Distillation

Weak-supervised Visual Geo-localization via Attention-based Knowledge Distillation Introduction WAKD is a PyTorch implementation for our ICPR-2022 pap

2 Oct 20, 2022
Code base for the paper "Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation"

This repository contains code for the paper Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiati

8 Aug 28, 2022
ElasticFace: Elastic Margin Loss for Deep Face Recognition

This is the official repository of the paper: ElasticFace: Elastic Margin Loss for Deep Face Recognition Paper on arxiv: arxiv Model Log file Pretrain

Fadi Boutros 113 Dec 14, 2022
基于Paddlepaddle复现yolov5,支持PaddleDetection接口

PaddleDetection yolov5 https://github.com/Sharpiless/PaddleDetection-Yolov5 简介 PaddleDetection飞桨目标检测开发套件,旨在帮助开发者更快更好地完成检测模型的组建、训练、优化及部署等全开发流程。 PaddleD

36 Jan 07, 2023
Generic Foreground Segmentation in Images

Pixel Objectness The following repository contains pretrained model for pixel objectness. Please visit our project page for the paper and visual resul

Suyog Jain 157 Nov 21, 2022
🛠️ Tools for Transformers compression using Lightning ⚡

Bert-squeeze is a repository aiming to provide code to reduce the size of Transformer-based models or decrease their latency at inference time.

Jules Belveze 66 Dec 11, 2022
用opencv的dnn模块做yolov5目标检测,包含C++和Python两个版本的程序

yolov5-dnn-cpp-py yolov5s,yolov5l,yolov5m,yolov5x的onnx文件在百度云盘下载, 链接:https://pan.baidu.com/s/1d67LUlOoPFQy0MV39gpJiw 提取码:bayj python版本的主程序是main_yolov5.

365 Jan 04, 2023
Python Fanduel API (2021) - Lineup Automation

Southpaw is a python package that provides access to the Fanduel API. Optimize your DFS experience by programmatically updating your lineups, analyzin

Brandin Canfield 13 Jan 04, 2023
Code for Environment Dynamics Decomposition (ED2).

ED2 Code for Environment Dynamics Decomposition (ED2). Installation Follow the installation in MBPO and Dreamer. Usage First follow the SD2 method for

0 Aug 10, 2021
Implementation of the bachelor's thesis "Real-time stock predictions with deep learning and news scraping".

Real-time stock predictions with deep learning and news scraping This repository contains a partial implementation of my bachelor's thesis "Real-time

David Álvarez de la Torre 0 Feb 09, 2022
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data.

causal-bald | Abstract | Installation | Example | Citation | Reproducing Results DUE An implementation of the methods presented in Causal-BALD: Deep B

OATML 13 Oct 07, 2022
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

DeCLIP Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm. Our paper is available in arxiv Updates ** Ou

Sense-GVT 470 Dec 30, 2022
Building Ellee — A GPT-3 and Computer Vision Powered Talking Robotic Teddy Bear With Human Level Conversation Intelligence

Using an object detection and facial recognition system built on MobileNetSSDV2 and Dlib and running on an NVIDIA Jetson Nano, a GPT-3 model, Google Speech Recognition, Amazon Polly and servo motors,

24 Oct 26, 2022
Event queue (Equeue) dialect is an MLIR Dialect that models concurrent devices in terms of control and structure.

Event Queue Dialect Event queue (Equeue) dialect is an MLIR Dialect that models concurrent devices in terms of control and structure. Motivation The m

Cornell Capra 23 Dec 08, 2022
Code for our paper "MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction" published at ICCV 2021.

MG-GAN: A Multi-Generator Model Preventing Out-of-Distribution Samples in Pedestrian Trajectory Prediction This repository contains the code for the p

Sven 30 Jan 05, 2023