Vincy2King's Stars
ultralytics/yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
PySimpleGUI/PySimpleGUI
Python GUIs for Humans! PySimpleGUI is the top-rated Python application development environment. Launched in 2018 and actively developed, maintained, and supported in 2024. Transforms tkinter, Qt, WxPython, and Remi into a simple, intuitive, and fun experience for both hobbyists and expert users.
PaddlePaddle/PaddleDetection
Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.
PetarV-/GAT
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning
Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
thunlp/Chinese_Rumor_Dataset
中文谣言数据
acl-org/aclpubcheck
Tools for checking ACL paper submissions
EscVM/Efficient-CapsNet
Official TensorFlow code for the paper "Efficient-CapsNet: Capsule Network with Self-Attention Routing".
THU-BPM/CHEF
The source code of paper "CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking"
GT-RIPL/Xmodal-Ctx
Official PyTorch implementation of our CVPR 2022 paper: Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning
MachineJeff/Chinese-Rumor-Recognition
Chinese Rumor Recognition 本科毕业设计论文-中文谣言检测
ccfddl/ccfddl.github.io
kakshak07/Image-Captioining
The objective is to process by generating textual description from an image – based on the objects and actions in the image. Using generative models so that it creates novel sentences. Pipeline type models uses two separate learning process, one for language modelling and other for image recognition. It first identifies objects in image and provides the result to the Inception-v3 model to convert into word embedding vector than into series of LSTM cells to get desired captions.
SenHe/Human-Attention-in-Image-Captioning
abhilashmnair/Sarcasm-Detection-with-BERT-and-GCN
A sarcasm detection model using Bidirectional Encoder Representations for Transformers (BERT) and Graph Convolutional Networks (GCN) has shown state-of-art results against conventional models and vanilla transformer-based approaches.
oraccc/Implementation-of-VGCN-BERT-for-Rumor-Detection
Implementation-of-VGCN-BERT-for-Rumour-Detection
27182812/ChineseBERT_paddle
用Paddle复现论文ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information(ACL2021)
dmg-illc/didec-seq-gen
Riadsala/SaccadicBiases