This repo contains the source code for paper: Dasong Gao, Chen Wang, Sebastian Scherer. “AirLoop: Lifelong Loop Closure Detection.” arXiv preprint arXiv:2109.08975 (2021). Demo Examples of loop closure detection on each dataset. Note that our model is able to handle cross-environment loop closure detection despite only trained in individual environments sequentially: Improved loop closure detection on TartanAir after extended training: Usage Dependencies Python >= 3.5 PyTorch < 1.8 OpenCV >= 3.4 NumPy >= 1.19 Matplotlib ConfigArgParse PyYAML    

Project Description: In this project, we will be using the live video feed from the camera to detect Faces. It will also detect some specific points such as ears, nose, lips and eyes. Requirements: Following modules need to be installed for it to work properly: OpenCV: OpenCV is a huge open-source library for computer vision, machine learning, and image processing. OpenCV supports a wide variety of programming languages like Python, C++, Java, etc. It can process images and videos to […]

Reading List for topics in Sound Event Detection Introduction Sound event detection aims at processing the continuous acoustic signal and converting it into symbolic descriptions of the corresponding sound events present at the auditory scene. Sound event detection can be utilized in a variety of applications, including context-based indexing and retrieval in multimedia databases, unobtrusive monitoring in health care, and surveillance. Recently (since 2017), to utilise large multimedia data available, learning acoustic information from weak annotations was formulated. This reading […]

An attempt to harness the power of Deep Learning to come up with a solution that can let us detect various classes of activities an infant, toddler or a baby is performing in real-time. This POC can then be published as an end-to-end deployable cloud project. The model does not restrict predictions for babies only, it is applicable to all entities that appears in a human posture. So temporary, this needs to be handled at project level. Special thanks to […]

Industrial KNN-based Anomaly Detection Industrial knn-based anomaly detection for images. Visit streamlit link to check out the demo. This repo aims to reproduce the results of the following KNN-based anomaly detection methods: SPADE (Cohen et al. 2021) – knn in z-space and distance to feature maps PaDiM* (Defard et al. 2020) – distance to multivariate Gaussian of feature maps PatchCore (Roth et al. 2021) – knn distance to avgpooled feature maps * actually does not have any knn mechanism, but […]

DA_detection Progressive Domain Adaptation for Object Detection Implementation of our paper Progressive Domain Adaptation for Object Detection, based on pytorch-faster-rcnn and PyTorch-CycleGAN. Paper Progressive Domain Adaptation for Object DetectionHan-Kai Hsu, Chun-Han Yao, Yi-Hsuan Tsai, Wei-Chih Hung, Hung-Yu Tseng, Maneesh Singh and Ming-Hsuan YangIEEE Winter Conference on Applications of Computer Vision (WACV), 2020. Please cite our paper if you find it useful for your research. @inproceedings{hsu2020progressivedet, author = {Han-Kai Hsu and Chun-Han Yao and Yi-Hsuan Tsai and Wei-Chih Hung and Hung-Yu […]

QueryDet-PyTorch This repository is the official implementation of our paper: QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection Requirement a. Install Pytorch 1.4 b. Install APEX for mixed precision training c. Install our Pytorch based sparse convolution toolkit d. Install the detectron2 toolkit. Note we build our approach based on version 0.2.1, you may follow the instructions to set environment configs e. Install the Detectron2_Backbone for usage of MobileNet and ShuffleNet f. Clone our repository and have fun […]

Multi-Scale Aligned Distillation for Low-Resolution Detection Lu Qi*, Jason Kuen*, Jiuxiang Gu, Zhe Lin, Yi Wang, Yukang Chen, Yanwei Li, Jiaya Jia This project provides an implementation for the CVPR 2021 paper “Multi-Scale Aligned Distillation for Low-Resolution Detection” based on Detectron2. MSAD targets to detect objects using low-resolution instead of high-resolution image. MSAD could obtain comparable performance in high-resolution image size. Our paper use Slimmable Neural Networks as our pretrained weight. Installation This project is based on Detectron2, which can […]