Code for our CVPR 2022 Paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection"

Related tags

Deep Learninggen-vlkt
Overview

GEN-VLKT

Code for our CVPR 2022 paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection".

Contributed by Yue Liao*, Aixi Zhang*, Miao Lu, Yongliang Wang, Xiaobo Li and Si Liu.

Installation

Installl the dependencies.

pip install -r requirements.txt

Clone and build CLIP.

git clone https://github.com/openai/CLIP.git && cd CLIP && python setup.py develop && cd ..

Data preparation

HICO-DET

HICO-DET dataset can be downloaded here. After finishing downloading, unpack the tarball (hico_20160224_det.tar.gz) to the data directory.

Instead of using the original annotations files, we use the annotation files provided by the PPDM authors. The annotation files can be downloaded from here. The downloaded annotation files have to be placed as follows.

data
 └─ hico_20160224_det
     |─ annotations
     |   |─ trainval_hico.json
     |   |─ test_hico.json
     |   └─ corre_hico.npy
     :

V-COCO

First clone the repository of V-COCO from here, and then follow the instruction to generate the file instances_vcoco_all_2014.json. Next, download the prior file prior.pickle from here. Place the files and make directories as follows.

GEN-VLKT
 |─ data
 │   └─ v-coco
 |       |─ data
 |       |   |─ instances_vcoco_all_2014.json
 |       |   :
 |       |─ prior.pickle
 |       |─ images
 |       |   |─ train2014
 |       |   |   |─ COCO_train2014_000000000009.jpg
 |       |   |   :
 |       |   └─ val2014
 |       |       |─ COCO_val2014_000000000042.jpg
 |       |       :
 |       |─ annotations
 :       :

For our implementation, the annotation file have to be converted to the HOIA format. The conversion can be conducted as follows.

PYTHONPATH=data/v-coco \
        python convert_vcoco_annotations.py \
        --load_path data/v-coco/data \
        --prior_path data/v-coco/prior.pickle \
        --save_path data/v-coco/annotations

Note that only Python2 can be used for this conversion because vsrl_utils.py in the v-coco repository shows a error with Python3.

V-COCO annotations with the HOIA format, corre_vcoco.npy, test_vcoco.json, and trainval_vcoco.json will be generated to annotations directory.

Pre-trained model

Download the pretrained model of DETR detector for ResNet50, and put it to the params directory.

python ./tools/convert_parameters.py \
        --load_path params/detr-r50-e632da11.pth \
        --save_path params/detr-r50-pre-2branch-hico.pth \
        --num_queries 64

python ./tools/convert_parameters.py \
        --load_path params/detr-r50-e632da11.pth \
        --save_path params/detr-r50-pre-2branch-vcoco.pth \
        --dataset vcoco \
        --num_queries 64

Training

After the preparation, you can start training with the following commands. The whole training is split into two steps: GEN-VLKT base model training and dynamic re-weighting training. The trainings of GEN-VLKT-S for HICO-DET and V-COCO are shown as follows.

HICO-DET

sh ./config/hico_s.sh

V-COCO

sh ./configs/vcoco_s.sh

Zero-shot

sh ./configs/hico_s_zs_nf_uc.sh

Evaluation

HICO-DET

You can conduct the evaluation with trained parameters for HICO-DET as follows.

python -m torch.distributed.launch \
        --nproc_per_node=8 \
        --use_env \
        main.py \
        --pretrained pretrained/hico_gen_vlkt_s.pth \
        --dataset_file hico \
        --hoi_path data/hico_20160224_det \
        --num_obj_classes 80 \
        --num_verb_classes 117 \
        --backbone resnet50 \
        --num_queries 64 \
        --dec_layers 3 \
        --eval \
        --with_clip_label \
        --with_obj_clip_label \
        --use_nms_filter

For the official evaluation (reported in paper), you need to covert the prediction file to a official prediction format following this file, and then follow PPDM evaluation steps.

V-COCO

Firstly, you need the add the following main function to the vsrl_eval.py in data/v-coco.

if __name__ == '__main__':
  import sys

  vsrl_annot_file = 'data/vcoco/vcoco_test.json'
  coco_file = 'data/instances_vcoco_all_2014.json'
  split_file = 'data/splits/vcoco_test.ids'

  vcocoeval = VCOCOeval(vsrl_annot_file, coco_file, split_file)

  det_file = sys.argv[1]
  vcocoeval._do_eval(det_file, ovr_thresh=0.5)

Next, for the official evaluation of V-COCO, a pickle file of detection results have to be generated. You can generate the file with the following command. and then evaluate it as follows.

python generate_vcoco_official.py \
        --param_path pretrained/VCOCO_GEN_VLKT_S.pth \
        --save_path vcoco.pickle \
        --hoi_path data/v-coco \
        --num_queries 64 \
        --dec_layers 3 \
        --use_nms_filter \
        --with_clip_label \
        --with_obj_clip_label

cd data/v-coco
python vsrl_eval.py vcoco.pickle

Zero-shot

python -m torch.distributed.launch \
        --nproc_per_node=8 \
        --use_env \
        main.py \
        --pretrained pretrained/hico_gen_vlkt_s.pth \
        --dataset_file hico \
        --hoi_path data/hico_20160224_det \
        --num_obj_classes 80 \
        --num_verb_classes 117 \
        --backbone resnet50 \
        --num_queries 64 \
        --dec_layers 3 \
        --eval \
        --with_clip_label \
        --with_obj_clip_label \
        --use_nms_filter \
        --zero_shot_type rare_first \
        --del_unseen

Regular HOI Detection Results

HICO-DET

Full (D) Rare (D) Non-rare (D) Full(KO) Rare (KO) Non-rare (KO) Download Conifg
GEN-VLKT-S (R50) 33.75 29.25 35.10 36.78 32.75 37.99 model config
GEN-VLKT-M* (R101) 34.63 30.04 36.01 37.97 33.72 39.24 model config
GEN-VLKT-L (R101) 34.95 31.18 36.08 38.22 34.36 39.37 model config

D: Default, KO: Known object, *: The original model is lost and the provided checkpoint performance is slightly different from the paper reported.

V-COCO

Scenario 1 Scenario 2 Download Config
GEN-VLKT-S (R50) 62.41 64.46 model config
GEN-VLKT-M (R101) 63.28 65.58 model config
GEN-VLKT-L (R101) 63.58 65.93 model config

Zero-shot HOI Detection Results

Type Unseen Seen Full Download Conifg
GEN-VLKT-S RF-UC 21.36 32.91 30.56 model config
GEN-VLKT-S NF-UC 25.05 23.38 23.71 model config
GEN-VLKT-S UO 10.51 28.92 25.63 model config
GEN-VLKT-S UV 20.96 30.23 28.74 model config

Citation

Please consider citing our paper if it helps your research.

@inproceedings{liao2022genvlkt,
  title={GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection},
  author={Yue Liao, Aixi Zhang, Miao Lu, Yongliang Wang, Xiaobo Li, Si Liu},
  booktitle={CVPR},
  year={2022}
}

License

GEN-VLKT is released under the MIT license. See LICENSE for additional details.

Acknowledge

Some of the codes are built upon PPDM, DETR, QPIC and CDN. Thanks them for their great works!

Owner
Yue Liao
PhD candidate at Beihang University
Yue Liao
GitHub Repository
Real-Time Social Distance Monitoring tool using Computer Vision

Social Distance Detector A Real-Time Social Distance Monitoring Tool Table of Contents Motivation YOLO Theory Detection Output Tech Stack Functionalit

Pranav B 13 Oct 14, 2022
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (CVPR'2020)

Under-exposure introduces a series of visual degradation, i.e. decreased visibility, intensive noise, and biased color, etc. To address these problems, we propose a novel semi-supervised learning app

Yang Wenhan 117 Jan 03, 2023
Code for paper "A Critical Assessment of State-of-the-Art in Entity Alignment" (https://arxiv.org/abs/2010.16314)

A Critical Assessment of State-of-the-Art in Entity Alignment This repository contains the source code for the paper A Critical Assessment of State-of

Max Berrendorf 16 Oct 14, 2022
A selection of State Of The Art research papers (and code) on human locomotion (pose + trajectory) prediction (forecasting)

A selection of State Of The Art research papers (and code) on human trajectory prediction (forecasting). Papers marked with [W] are workshop papers.

Karttikeya Manglam 40 Nov 18, 2022
An exploration of log domain "alternative floating point" for hardware ML/AI accelerators.

This repository contains the SystemVerilog RTL, C++, HLS (Intel FPGA OpenCL to wrap RTL code) and Python needed to reproduce the numerical results in

Facebook Research 373 Dec 31, 2022
Official PyTorch implementation of MAAD: A Model and Dataset for Attended Awareness

MAAD: A Model for Attended Awareness in Driving Install // Datasets // Training // Experiments // Analysis // License Official PyTorch implementation

7 Oct 16, 2022
AI-based, context-driven network device ranking

Batea A batea is a large shallow pan of wood or iron traditionally used by gold prospectors for washing sand and gravel to recover gold nuggets. Batea

Secureworks Taegis VDR 269 Nov 26, 2022
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Segmentation Transformer Implementation of Segmentation Transformer in PyTorch, a new model to achieve SOTA in semantic segmentation while using trans

Abhay Gupta 161 Dec 08, 2022
Benchmark tools for Compressive LiDAR-to-map registration

Benchmark tools for Compressive LiDAR-to-map registration This repo contains the released version of code and datasets used for our IROS 2021 paper: "

Allie 9 Nov 24, 2022
Fine-Tune EleutherAI GPT-Neo to Generate Netflix Movie Descriptions in Only 47 Lines of Code Using Hugginface And DeepSpeed

GPT-Neo-2.7B Fine-Tuning Example Using HuggingFace & DeepSpeed Installation cd venv/bin ./pip install -r ../../requirements.txt ./pip install deepspe

Nikita 180 Jan 05, 2023
A GUI for Face Recognition, based upon Docker, Tkinter, GPU and a camera device.

Face Recognition GUI This repository is a GUI version of Face Recognition by Adam Geitgey, where e.g. Docker and Tkinter are utilized. All the materia

Kasper Henriksen 6 Dec 05, 2022
Pre-trained NFNets with 99% of the accuracy of the official paper

NFNet Pytorch Implementation This repo contains pretrained NFNet models F0-F6 with high ImageNet accuracy from the paper High-Performance Large-Scale

Benjamin Schmidt 133 Dec 09, 2022
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains

Open-Domain Question-Answering for COVID-19 and Other Emergent Domains This repository contains the source code for an end-to-end open-domain question

7 Sep 27, 2022
Complete system for facial identity system

Complete system for facial identity system. Include one-shot model, database operation, features visualization, monitoring

4 May 02, 2022
A generalist algorithm for cell and nucleus segmentation.

Cellpose | A generalist algorithm for cell and nucleus segmentation. Cellpose was written by Carsen Stringer and Marius Pachitariu. To learn about Cel

MouseLand 733 Dec 29, 2022
CONditionals for Ordinal Regression and classification in PyTorch

CONDOR pytorch implementation for ordinal regression with deep neural networks. Documentation: https://GarrettJenkinson.github.io/condor_pytorch About

7 Jul 25, 2022
Unet network with mean teacher for altrasound image segmentation

Unet network with mean teacher for altrasound image segmentation

5 Nov 21, 2022
DexterRedTool - Dexter's Red Team Tool that creates cronjob/task scheduler to consistently creates users

DexterRedTool Author: Dexter Delandro CSEC 473 - Spring 2022 This tool persisten

2 Feb 16, 2022
Advanced yabai wooting scripts

Yabai Wooting scripts Installation requirements Both https://github.com/xiamaz/python-yabai-client and https://github.com/xiamaz/python-wooting-rgb ne

Max Zhao 3 Dec 31, 2021
Fantasy Points Prediction and Dream Team Formation

Fantasy-Points-Prediction-and-Dream-Team-Formation Collected Data from open source resources that have over 100 Parameters for predicting cricket play

Akarsh Singh 2 Sep 13, 2022