Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos Created by Muheng Li, Lei Chen, Yueqi Duan, Zhilan Hu, Jianjiang Feng, Jie

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Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification Implementation of "Learning From Multiple Experts: Se

CPM CPM is an open-source program on large-scale pre-trained models, which is conducted by Beijing Academy of Artificial Intelligence and Tsinghua Uni

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Neural Networks from Scratch (NNFS) book code Code from the NNFS book (https://nnfs.io) separated by chapter.

A script written in Python that returns a consensus string and profile matrix of a given DNA string(s) in FASTA format.

Transformer Hawkes Process Source code for Transformer Hawkes Process (ICML 2020). Run the code Dependencies Python 3.7. Anaconda contains all the req

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Pixel-level Self-Paced Learning for Super-Resolution This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resoluti

DSN-IQA Source code for paper "Deep Superpixel-based Network for Blind Image Quality Assessment" Requirements Python =3.8.0 Pytorch =1.7.1 Usage wit

SynthSR This repository contains code to train a Convolutional Neural Network (CNN) for Super-resolution (SR), or joint SR and data synthesis. The met

PCGNN - Procedural Content Generation with NEAT and Novelty Generation Approach — Metrics — Paper — Poster — Examples PCGNN - Procedural Content Gener

Finetune the base 64 px GLIDE-text2im model from OpenAI on your own image-text dataset

Searching for Efficient Multi-Stage Vision Transformers This repository contains the official Pytorch implementation of "Searching for Efficient Multi

trt_pose Want to detect hand poses? Check out the new trt_pose_hand project for real-time hand pose and gesture recognition! trt_pose is aimed at enab

Learning Saliency Propagation for Semi-supervised Instance Segmentation PyTorch Implementation This repository contains: the PyTorch implementation of

This is the code used in the paper "Entity Embeddings of Categorical Variables". If you want to get the original version of the code used for the Kagg