This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning

Bottom-up attention model for image captioning and VQA, based on Faster R-CNN and Visual Genome

Object detection with multi-level representations generated from deep high-resolution representation learning (HRNetV2h). This is an official implementation for our TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition". https://arxiv.org/abs/1908.07919

This repository contains the source code of our work on designing efficient CNNs for computer vision

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

Video Platform for Action Recognition and Object Detection in Pytorch

VarifocalNet: An IoU-aware Dense Object Detector

SWA Object Detection

Boundary-preserving Mask R-CNN (ECCV 2020)

High-resolution Networks for the Fully Convolutional One-Stage Object Detection (FCOS) algorithm

Semantic Propositional Image Caption Evaluation

A tensorflow implement mobilenetv3 centernet, which can be easily deployeed on android(MNN) and ios(CoreML).

generate captions for images using a CNN-RNN model that is trained on the Microsoft Common Objects in COntext (MS COCO) dataset

Adds SPICE metric to coco-caption evaluation server codes

Visually informed embedding of word (VIEW) is a tool for transferring multimodal background knowledge to NLP algorithms.

Implementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models

A Keras implementation of DeepMask based on NIPS 2015 paper "Learning to Segment Object Candidates"

We aim to generate realistic images from text descriptions using GAN architecture. The network that we have designed is used for image generation for two datasets: MSCOCO and CUBS.

Clone of COCO API - Dataset @ http://cocodataset.org/ - with changes to support Windows build and python3

The Jakarnotator is an annotation tool to create your own database for instance segmentation problem.

Hindi Image Captioning

Train Azure Custom Vision Service using COCO dataset

Preserving Semantic Neighborhoods for Robust Cross-modal Retrieval [ECCV 2020]

Official code for “Cascaded Context Dependency: An Extremely Lightweight Module for Deep Convolutional Neural Networks”

A demo for mapping class labels from ImageNet to COCO.

Inpainting on MSCOCO

EDGAN: StackGAN with Embedding Distance Training

Implementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models

Image Captioning on Microsoft Coco Dataset