Pinned Repositories
-baseline
高鲁棒性要求下的领域事件检测任务baseline，转化为ner的形式做任务
AMSR-Few-Shot
Adapting Multi-source Representations for Cross-Domain Few-shot Learning (CD-FSL)
awesome-self-supervised-learning
A curated list of awesome self-supervised methods
building-height-deu
D. Frantz, F. Schug, A. Okujeni, C. Navacchi, W. Wagner, S. van der Linden, and P. Hostert (2021): National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment 252, 112128. https://doi.org/10.1016/j.rse.2020.112128
ChineseToImg
Python3生成汉字字库并转换为图片
CMIR-NET-A-deep-learning-based-model-for-cross-modal-retrieval-in-remote-sensing
We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multispectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech-based label annotations. These multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the increasing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network-based architecture that is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multispectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases.
CPU-GPU-Benchmark
CPU-Benchmark CPU单核多核跑分图
DeepLearningNote
this is all of my code and data with my deep learning note
Demo_DHCNN_for_TGRS2021
A novel deep hashing method (DHCNN) for remote sensing image retrieval and classification, which was pulished in IEEE Trans. Geosci. Remote Sens., 2021.
Traditional-Feature-Extraction-Methods
Feature Extraction is an integral step for Image Processing jobs. This repository contains the python codes for Traditonal Feature Extraction Methods from an image dataset, namely Gabor, Haralick, Tamura, GLCM and GLRLM.
JonasZero's Repositories
JonasZero/Traditional-Feature-Extraction-Methods
Feature Extraction is an integral step for Image Processing jobs. This repository contains the python codes for Traditonal Feature Extraction Methods from an image dataset, namely Gabor, Haralick, Tamura, GLCM and GLRLM.
JonasZero/-baseline
高鲁棒性要求下的领域事件检测任务baseline，转化为ner的形式做任务
JonasZero/AMSR-Few-Shot
Adapting Multi-source Representations for Cross-Domain Few-shot Learning (CD-FSL)
JonasZero/awesome-self-supervised-learning
A curated list of awesome self-supervised methods
JonasZero/building-height-deu
D. Frantz, F. Schug, A. Okujeni, C. Navacchi, W. Wagner, S. van der Linden, and P. Hostert (2021): National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment 252, 112128. https://doi.org/10.1016/j.rse.2020.112128
JonasZero/ChineseToImg
Python3生成汉字字库并转换为图片
JonasZero/CMIR-NET-A-deep-learning-based-model-for-cross-modal-retrieval-in-remote-sensing
We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multispectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech-based label annotations. These multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the increasing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network-based architecture that is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multispectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases.
JonasZero/CPU-GPU-Benchmark
CPU-Benchmark CPU单核多核跑分图
JonasZero/DeepLearningNote
this is all of my code and data with my deep learning note
JonasZero/Demo_DHCNN_for_TGRS2021
A novel deep hashing method (DHCNN) for remote sensing image retrieval and classification, which was pulished in IEEE Trans. Geosci. Remote Sens., 2021.
JonasZero/fast-reid
SOTA Re-identification Methods and Toolbox
JonasZero/Geodjango-Vue-Leaflet-Demo
The project shows how we can build an API using Django/GeoDjango, the Django Rest framework, Django-rest-framework-gis, and output data (from a PostgreSQL database) in a format that is GeoJSON compatible. The API is used in a Vue application which displays data randomly on a web map (Leaflet) using polling.
JonasZero/HanLP
中文分词 词性标注 命名实体识别 依存句法分析 语义依存分析 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
JonasZero/helloworld
JonasZero/Image_match
An image retrieval and matching method based on color histogram.
JonasZero/leeml-notes
李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes
JonasZero/LSH_PyTorch
Source code for paper "Similarity Search in High Dimensions via Hashing" on VLDH-1999
JonasZero/map-marker-openlayers
OpenLayers map marker popup. Map delivery area. Find location from address, geolocation. Multiple markers with html popups. Import and export polygon with openlayers map..
JonasZero/ml4a-guides
practical guides, tutorials, and code samples for ml4a
JonasZero/mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
JonasZero/Multi-source-domain-adapation
JonasZero/OpenSelfSup
Self-Supervised Learning Toolbox and Benchmark
JonasZero/PaddleClas
A treasure chest for visual recognition powered by PaddlePaddle
JonasZero/PyRetri
Open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥
JonasZero/sat_to_map
Learning mappings to generate city maps images from corresponding satellite images.
JonasZero/ssc_csharp
C# version of Sound Shape Code(SSC)
JonasZero/test
JonasZero/VisualTransformers
A Pytorch Implementation of the following paper "Visual Transformers: Token-based Image Representation and Processing for Computer Vision"
JonasZero/wsyinstalliing.github.io