Deep Learning, NLP, NMT, AI, ML
Give real chat boosting to your discord server. ✅ Setup: – Add token to scrape messages on server that you whant. – Put the token in config file. – Put the channel id to scrape messages, if you whant to scrape message from any servers and any channels, enable `capture_all`
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When you’re automating some task, for example packaging your application for Docker, you’ll often find yourself writing shell scripts. You might have a bash script to drive the packaging process, and another script as an entry point for the container. As your packaging grows in complexity, so does your shell script. Everything works fine. And then, one day, your shell script does something completely wrong. That’s when you realize your mistake: bash, and shell scripting languages in general, are mostly […]
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This is the first released system towards detection and recognition of complex meters in wild. The system can be divided into three moduels. Fisrtly, a yolo-based detector is applied to get pure meter region. Secondly, a spatial transformer module is eatablished to rectify the position of meter. Lastly, an end-to-end network is to read meter values, which is implemented by pointer/dail predcition and key number learning. Visulization results Left row is the original image, middle row is the process of […]
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by: Mohamed Ragab*, Emadeldeen Eldele*, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee Kwoh, Xiaoli Li AdaTime is a PyTorch suite to systematically and fairly evaluate different domain adaptation methods on time series data. Requirmenets: Python3 Pytorch==1.7 Numpy==1.20.1 scikit-learn==0.24.1 Pandas==1.2.4 skorch==0.10.0 (For DEV risk calculations) openpyxl==3.0.7 (for classification reports) Wandb=0.12.7 (for sweeps) Datasets Available Datasets We used four public datasets in this study. We also provide the preprocessed versions as follows: Adding New Dataset Structure of data To […]
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By Wen Wang, Jing Zhang, Yang Cao, Yongliang Shen, and Dacheng Tao This repository is an official implementation of DE-CondDETR and DELA-CondDETR in the paper Towards Data-Efficient Detection Transformers. For the implementation of DE-DETR and DELA-DETR, please refer to DE-DETRs. Introduction TL; DR. We identify the data-hungry issue of existing detection transformers and alleviate it by simply alternating how key and value sequences are constructed in the cross-attention layer, with minimum modifications to the original models. Besides, we introduce a […]
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This repository is an official PyTorch implementation of paper: Local-Global Context Aware Transformer for Language-Guided Video Segmentation. Chen Liang, Wenguan Wang, Tianfei Zhou, Jiaxu Miao, Yawei Luo, Yi Yang arXiv 2022. News & Update Logs: [2022-03-17] Repo created. Paper, code, and data will come in a few days. Stay tuned. [2022-03-18] Inference code, pretrained weights, and data for A2D-S+ released. [2022-03-21] arXiv (full paper available) Instructions on usage Training code and detailed instructions Code for dataset creation Abstract We explore […]
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This repo hosts the code for implementing the DW, as presented in our CVPR 2022 paper. Introduction Label assignment (LA), which aims to assign each training sample a positive (pos) and a negative (neg) loss weight, plays an important role in object detection. Existing LA methods mostly focus on the design of pos weighting function, while the neg weight is directly derived from the pos weight. Such a mechanism limits the learning capacity of detectors. In this paper, we explore […]
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VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention Shengheng Deng, Zhihao Liang, Lin Sun and Kui Jia* (*) Corresponding author Introduction Detecting objects from LiDAR point clouds is of tremendous significance in autonomous driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved due to the sparsity and irregularity of LiDAR point clouds. Among existing strategies, multi-view methods have shown great promise by leveraging the more comprehensive information from both bird’s eye […]
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Cube = { verts = {{0,0,0},{0,0,1},{0,1,0}…}, faces = {}, cols = {}, fcols = {}, } Download export_lua.py. In Blender, go to Edit > Preference > Addon > Install and choose export_lua.py. Tick the checkbox in the addon pannel to activate it. The option for exporting to LUA is located in File > Export > Export LUA.
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This repository provides a minimal example of NVIDIA’s Isaac Gym, to assist other researchers like me to quickly understand the code structure, to be able to design fully customised large-scale reinforcement learning experiments. The example is based on the official implementation from the Isaac Gym’s Benchmark Experiments, for which we have followed a similar implementation on Cartpole, but with a minimal number of lines of code aiming for maximal readability, and without using any third-party RL frameworks. Note: The current […]
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