SAAVN - Sound Adversarial Audio-Visual Navigation,ICLR2022 (In PyTorch)

Related tags

Deep LearningSAAVN
Overview

SAAVN

SAAVN Code release for paper "Sound Adversarial Audio-Visual Navigation,ICLR2022" (In PyTorch)

These code are under cleaning! Some of bugs maybe happen, please tell me if you have any trouble.

Thanks

These codes are based on the SoundSpaces code base.

Usage

This repo supports AudioGoal Task on Replica and Matterport3D datasets.

Below we show the commands for training and evaluating AudioGoal with Depth sensor on Replica, but it applies to Matterport dataset as well.

  1. Training
python main.py --default av_nav --run-type train --exp-config [exp_config_file] --model-dir data/models/replica/av_nav/e0000/audiogoal_depth --tag-config [tag_config_file] TORCH_GPU_ID 0 SIMULATOR_GPU_ID 0
  1. Validation (evaluate each checkpoint and generate a validation curve)
python main.py --default av_nav --run-type eval --exp-config [exp_config_file] --model-dir data/models/replica/av_nav/e0000/audiogoal_depth --tag-config [tag_config_file] TORCH_GPU_ID 0 SIMULATOR_GPU_ID 0
  1. Test the best validation checkpoint based on validation curve
python main.py --default av_nav --run-type eval --exp-config [exp_config_file] --model-dir data/models/replica/av_nav/e0000/audiogoal_depth --tag-config [tag_config_file] TORCH_GPU_ID 0 SIMULATOR_GPU_ID 0
  1. Generate demo video with audio
python main.py --default av_nav --run-type eval --exp-config [exp_config_file] --model-dir data/models/replica/av_nav/e0000/audiogoal_depth --tag-config [tag_config_file] TORCH_GPU_ID 0 SIMULATOR_GPU_ID 0

Note: [exp_config_file] is the main parameter configuration file of the experiment, while [tag_config_file] is special parameter configuration file for abalation experiments.

Citation

If you use this model in your research, please cite the following paper:

@inproceedings{YinfengICLR2022saavn,
	title = {Sound Adversarial Audio-Visual Navigation},
	author = {Yinfeng Yu, Wenbing Huang, Fuchun Sun, Changan Chen, Yikai Wang, Xiaohong Liu},
	year = {2022},
        booktitle={ICLR},
}
Owner
YinfengYu
YinfengYu
GitHub Repository
Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018

Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples This project is for the paper "Training Confidence-Calibrated Clas

168 Nov 29, 2022
More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval

More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval, CVPR 2021. Ayan Kumar Bhunia, Pinaki nath Chowdh

Ayan Kumar Bhunia 22 Aug 27, 2022
StrongSORT: Make DeepSORT Great Again

StrongSORT StrongSORT: Make DeepSORT Great Again StrongSORT: Make DeepSORT Great Again Yunhao Du, Yang Song, Bo Yang, Yanyun Zhao arxiv 2202.13514 Abs

369 Jan 04, 2023
ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

ALFRED A Benchmark for Interpreting Grounded Instructions for Everyday Tasks Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han,

ALFRED 204 Dec 15, 2022
A2LP for short, ECCV2020 spotlight, Investigating SSL principles for UDA problems

Label-Propagation-with-Augmented-Anchors (A2LP) Official codes of the ECCV2020 spotlight (label propagation with augmented anchors: a simple semi-supe

20 Oct 27, 2022
PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

Daft-Exprt - PyTorch Implementation PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis The

Keon Lee 47 Dec 18, 2022
The project is associated with the recently-launched ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) to provide participants with baseline systems for speech recognition and speaker diarization in conference scenario.

M2MeT challenge baseline -- AliMeeting This project provides the baseline system recipes for the ICASSP 2020 Multi-channel Multi-party Meeting Transcr

yufan 81 Dec 08, 2022
One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing".

Introduction One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing". Users

seq-to-mind 18 Dec 11, 2022
Code for the paper "Relation of the Relations: A New Formalization of the Relation Extraction Problem"

This repo contains the code for the EMNLP 2020 paper "Relation of the Relations: A New Paradigm of the Relation Extraction Problem" (Jin et al., 2020)

YYY 27 Oct 26, 2022
A python implementation of Deep-Image-Analogy based on pytorch.

Deep-Image-Analogy This project is a python implementation of Deep Image Analogy.https://arxiv.org/abs/1705.01088. Some results Requirements python 3

Peng Lu 171 Dec 14, 2022
2021搜狐校园文本匹配算法大赛 分比我们低的都是帅哥队

sohu_text_matching 2021搜狐校园文本匹配算法大赛Top2：分比我们低的都是帅哥队 本repo包含了本次大赛决赛环节提交的代码文件及答辩PPT，提交的模型文件可在百度网盘获取（链接：https://pan.baidu.com/s/1T9FtwiGFZhuC8qqwXKZSNA ，

hflserdaniel 43 Oct 01, 2022
Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022.

Jadena Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022. arXiv

Qing Guo 13 Nov 29, 2022
Official implement of Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer

Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer This repository contains the PyTorch code for Evo-ViT. This work proposes a slow-fas

YifanXu 53 Dec 05, 2022
Lane assist for ETS2, built with the ultra-fast-lane-detection model.

Euro-Truck-Simulator-2-Lane-Assist Lane assist for ETS2, built with the ultra-fast-lane-detection model. This project was made possible by the amazing

36 Jan 05, 2023
This repo is for segmentation of T2 hyp regions in gliomas.

T2-Hyp-Segmentor This repo is for segmentation of T2 hyp regions in gliomas. By downloading the model from here you can use it to segment your T2w ima

1 Jan 18, 2022
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetu

3 Dec 05, 2022
Contains supplementary materials for reproduce results in HMC divergence time estimation manuscript

Scalable Bayesian divergence time estimation with ratio transformations This repository contains the instructions and files to reproduce the analyses

Suchard Research Group 1 Sep 21, 2022
Identify the emotion of multiple speakers in an Audio Segment

MevonAI - Speech Emotion Recognition Identify the emotion of multiple speakers in a Audio Segment Report Bug · Request Feature Try the Demo Here Table

Suyash More 110 Dec 03, 2022
Deep Learning Training Scripts With Python

Deep Learning Training Scripts DNN Frameworks Caffe PyTorch Tensorflow CNN Models VGG ResNet DenseNet Inception Language Modeling GatedCNN-LM Attentio

Multicore Computing Research Lab 16 Dec 15, 2022
Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation（Unfinished）

Keras-FCN Fully convolutional networks and semantic segmentation with Keras. Models Models are found in models.py, and include ResNet and DenseNet bas

645 Dec 29, 2022