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Source code for the CVPR'20 paper "Blindly Assess Image Quality in the Wild Guided by A Self-Adaptive Hyper Network"
收集 CVPR 最新的成果，包括论文、代码和demo视频等，欢迎大家推荐！Collect the latest CVPR (Conference on Computer Vision and Pattern Recognition) results, including papers, code, and demo videos, etc., and welcome recommendati…
SUNet: Swin Transformer with UNet for Image Denoising
参考论文：Guo Y, Chen J, Wang J, et al. Closed-loop matters: Dual regression networks for single image super-resolution[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognit…
PyTorch code for our ICLR 2019 paper "Residual Non-local Attention Networks for Image Restoration"
PyTorch code for our NeurIPS 2022 paper "Cross Aggregation Transformer for Image Restoration"
Super Resolving Digital Elevation Models using a U-Net based convolutional residual network trained adverserially and using an improved loss function.
Crossed Dual-branch U-Net for Hyperspectral Image Super-Resolution, JSTARS, 2023
Final project in deep learning. Super resolution on images using U-Net
[AAAI' 25] U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
The pytorch implementation of Multi-interactive Siamese Decoder for RGBT Salient Object Detection
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建，自动问答，基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务。
Paper and implementation of UNet-related model.
deep learning for image processing including classification and object-detection etc.
中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)
SoftVC VITS Singing Voice Conversion
so-vits-svc fork with realtime support, improved interface and more features.
[EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction
[ACL 2024] An Easy-to-use Instruction Processing Framework for LLMs.
Jiagu深度学习自然语言处理工具 知识图谱关系抽取 中文分词 词性标注 命名实体识别 情感分析 新词发现 关键词 文本摘要 文本聚类
[EMNLP 2020] OpenUE: An Open Toolkit of Universal Extraction from Text
An experiment and demo-level tool for text information extraction (event-triples extraction), which can be a route to the event chain and topic graph, 基于依存句法与语义角色标注的事件三元组抽取，可用于文本理解如文档主题链，事件线等应用。