Official Implementation of VAT

Overview

Semantic correspondence

PWC PWC PWC

Few-shot segmentation

PWC PWC PWC PWC PWC PWC

Cost Aggregation Is All You Need for Few-Shot Segmentation

For more information, check out project [Project Page] and the paper on [arXiv].

Network

Our model VAT is illustrated below:

alt text

Environment Settings

git clone https://github.com/Seokju-Cho/Volumetric-Aggregation-Transformer.git

cd Volumetric-Aggregation-Transformer

conda env create -f environment.yaml

Preparing Few-Shot Segmentation Datasets

Download following datasets:

1. PASCAL-5i

Download PASCAL VOC2012 devkit (train/val data):

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar

Download PASCAL VOC2012 SDS extended mask annotations from our [Google Drive].

2. COCO-20i

Download COCO2014 train/val images and annotations:

wget http://images.cocodataset.org/zips/train2014.zip
wget http://images.cocodataset.org/zips/val2014.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip

Download COCO2014 train/val annotations from our Google Drive: [train2014.zip], [val2014.zip]. (and locate both train2014/ and val2014/ under annotations/ directory).

3. FSS-1000

Download FSS-1000 images and annotations from our [Google Drive].

Create a directory '../Datasets_VAT' for the above three few-shot segmentation datasets and appropriately place each dataset to have following directory structure:

../                         # parent directory
└── Datasets_VAT/
    ├── VOC2012/            # PASCAL VOC2012 devkit
    │   ├── Annotations/
    │   ├── ImageSets/
    │   ├── ...
    │   └── SegmentationClassAug/
    ├── COCO2014/           
    │   ├── annotations/
    │   │   ├── train2014/  # (dir.) training masks (from Google Drive) 
    │   │   ├── val2014/    # (dir.) validation masks (from Google Drive)
    │   │   └── ..some json files..
    │   ├── train2014/
    │   └── val2014/
    └── FSS-1000/           # (dir.) contains 1000 object classes
        ├── abacus/   
        ├── ...
        └── zucchini/

Training

Training on PASCAL-5i:

  python train.py --config "config/pascal_resnet{50, 101}/pascal_resnet{50, 101}_fold{0, 1, 2, 3}/config.yaml"

Training on COCO-20i:

  python train.py --config "config/coco_resnet50/coco_resnet50_fold{0, 1, 2, 3}/config.yaml"

Training on FSS-1000:

  python train.py --config "config/fss_resnet{50, 101}/config.yaml"

Evaluation

alt text

  • Download pre-trained weights on Link

Result on PASCAL-5i:

  python test.py --load "/path_to_pretrained_model/pascal_resnet{50, 101}/pascal_resnet{50, 101}_fold{0, 1, 2, 3}/"

Result on COCO-20i:

  python test.py --load "/path_to_pretrained_model/coco_resnet50/coco_resnet50_fold{0, 1, 2, 3}/"

Results on FSS-1000:

  python test.py --load "/path_to_pretrained_model/fss_resnet{50, 101}/"

Acknowledgement

We borrow code from public projects (huge thanks to all the projects). We mainly borrow code from HSNet.

Owner
Hamacojr
Hamacojr
Code repository for "Free View Synthesis", ECCV 2020.

Free View Synthesis Code repository for "Free View Synthesis", ECCV 2020. Setup Install the following Python packages in your Python environment - num

Intelligent Systems Lab Org 253 Dec 07, 2022
Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020)

Causality In Traffic Accident (Under Construction) Repository for Traffic Accident Benchmark for Causality Recognition (ECCV 2020) Overview Data Prepa

Tackgeun 21 Nov 20, 2022
A whale detector design for the Kaggle whale-detector challenge!

CNN (InceptionV1) + STFT based Whale Detection Algorithm So, this repository is my PyTorch solution for the Kaggle whale-detection challenge. The obje

Tarin Ziyaee 92 Sep 28, 2021
Code for "CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds" @ICRA2021

CloudAAE This is an tensorflow implementation of "CloudAAE: Learning 6D Object Pose Regression with On-line Data Synthesis on Point Clouds" Files log:

Gee 35 Nov 14, 2022
links and status of cool gradio demos

awesome-demos This is a list of some wonderful demos & applications built with Gradio. Here's how to contribute yours! 🖊️ Natural language processing

Gradio 96 Dec 30, 2022
Split Variational AutoEncoder

Split-VAE Split Variational AutoEncoder Introduction This repository contains and implemementation of a Split Variational AutoEncoder (SVAE). In a SVA

Andrea Asperti 2 Sep 02, 2022
torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations

💎A high level pipeline for face landmarks detection, supports training, evaluating, exporting, inference and 100+ data augmentations, compatible with torchvision and albumentations, can easily instal

DefTruth 142 Dec 25, 2022
[CVPR2021] Look before you leap: learning landmark features for one-stage visual grounding.

LBYL-Net This repo implements paper Look Before You Leap: Learning Landmark Features For One-Stage Visual Grounding CVPR 2021. Getting Started Prerequ

SVIP Lab 45 Dec 12, 2022
Code for "Primitive Representation Learning for Scene Text Recognition" (CVPR 2021)

Primitive Representation Learning Network (PREN) This repository contains the code for our paper accepted by CVPR 2021 Primitive Representation Learni

Ruijie Yan 76 Jan 02, 2023
Boundary-preserving Mask R-CNN (ECCV 2020)

BMaskR-CNN This code is developed on Detectron2 Boundary-preserving Mask R-CNN ECCV 2020 Tianheng Cheng, Xinggang Wang, Lichao Huang, Wenyu Liu Video

Hust Visual Learning Team 178 Nov 28, 2022
JAX + dataclasses

jax_dataclasses jax_dataclasses provides a wrapper around dataclasses.dataclass for use in JAX, which enables automatic support for: Pytree registrati

Brent Yi 35 Dec 21, 2022
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting

Pytorch Pedestrian Attribute Recognition: A strong PyTorch baseline of pedestrian attribute recognition and multi-label classification.

Jian 79 Dec 18, 2022
FPSAutomaticAiming——基于YOLOV5的FPS类游戏自动瞄准AI

FPSAutomaticAiming——基于YOLOV5的FPS类游戏自动瞄准AI 声明: 本项目仅限于学习交流,不可用于非法用途,包括但不限于:用于游戏外挂等,使用本项目产生的任何后果与本人无关! 简介 本项目基于yolov5,实现了一款FPS类游戏(CF、CSGO等)的自瞄AI,本项目旨在使用现

Fabian 246 Dec 28, 2022
DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021]

DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021] Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng

Automation and Intelligence for Civil Engineering (AI4CE) Lab @ NYU 98 Dec 21, 2022
LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

This project is based on ultralytics/yolov3. LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image. The related paper is avai

26 Dec 13, 2022
an Evolutionary Algorithm assisted GAN

EvoGAN an Evolutionary Algorithm assisted GAN ckpts

3 Oct 09, 2022
Pretty Tensor - Fluent Neural Networks in TensorFlow

Pretty Tensor provides a high level builder API for TensorFlow. It provides thin wrappers on Tensors so that you can easily build multi-layer neural networks.

Google 1.2k Dec 29, 2022
code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

75 Dec 16, 2022
Pytorch reimplementation of the Mixer (MLP-Mixer: An all-MLP Architecture for Vision)

MLP-Mixer Pytorch reimplementation of Google's repository for the MLP-Mixer (Not yet updated on the master branch) that was released with the paper ML

Eunkwang Jeon 18 Dec 08, 2022
Mitsuba 2: A Retargetable Forward and Inverse Renderer

Mitsuba Renderer 2 Documentation Mitsuba 2 is a research-oriented rendering system written in portable C++17. It consists of a small set of core libra

Mitsuba Physically Based Renderer 2k Jan 07, 2023