lpfworld Goto Github PK

aat

Code for paper "Adaptively Aligned Image Captioning via Adaptive Attention Time". NeurIPS 2019

asei

Adaptive Selection: External Knowledge and Internal Context Representation for Few-shot Intention Recognition

awesome-crowd-counting

Awesome Crowd Counting

awesome-image-captioning

A curated list of image captioning and related area resources. :-)

awesome-radiology-report-generation

A curated list of radiology report generation (medical report generation) and related areas. :-)

bert_sim

biobert

BioBERT: a pre-trained biomedical language representation model

biobert-pretrained

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

ce-unilm

chinesenmt

ChineseNMT: Translate English to Chinese with PyTorch Implementation of Transformer

context-aware-crowd-counting

Official Code for Context-Aware Crowd Counting. CVPR 2019

cvpr2021-papers-with-code

CVPR 2021 论文和开源项目合集

dcanet

Deep Connected Attention Networks

detectron2

Detectron2 is FAIR's next-generation platform for object detection and segmentation.

dmpnet

DMPNet

ernie

An Implementation of ERNIE For Language Understanding (including Pre-training models and Fine-tuning tools)

fidtm

Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd

goodnews

Good News Everyone! - CVPR 2019

lmc-memory

PyTorch Implementation of LMC-Memory (CVPR 2021 Oral)

manet

marunet

Multi-level Attention Refined UNet for crowd counting

medical-report-generation-using-deep-learning

Generating Medical Reports using Chest X-Rays

medical-transformer

Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"

mganet

nlp-tutorial

Natural Language Processing Tutorial for Deep Learning Researchers

object_relation_transformer

Implementation of the Object Relation Transformer for Image Captioning

papers

读过的CV方向的一些论文，图像生成文字、弱监督分割等

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

r2gen

setr

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers