[ICCV '21] In this repository you find the code to our paper "Keypoint Communities".

[CVPR 2022] Official Pytorch code for OW-DETR: Open-world Detection Transformer

A PyTorch implementation of the paper: Specifying Object Attributes and Relations in Interactive Scene Generation

Fast and accurate Human Pose Estimation using ShelfNet with PyTorch

Implementation for "Generating Multiple Objects at Spatially Distinct Locations" (ICLR 2019)

Code for "Semantic Object Accuracy for Generative Text-to-Image Synthesis" (TPAMI 2020)

Helper for dealing with MS-COCO annotations

[IJCAI 2022] Official Pytorch code for paper “S2 Transformer for Image Captioning”

[TIP] Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition

This Repo covers all formats of annotations for Object Detection and can easily convert from one form to another using attached scripts

Object Detection for Video with MXNet and GluonCV using YOLOv3

This repository contains code for YOLO v3 Object detection, and is capable of fast object detection. Input can be given through images, videos and webcam input feed.

Implementation of Darknet with You Only Look Once (YOLO) in Pytorch

[AAAI 2024] Official code for "Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection"

Ladder Loss for Coherent Visual-Semantic Embedding, AAAI, 2020

A deep learning based application which is entitled to help the visually impaired people. The application automatically generates the textual description of what's happening in front of the camera and conveys it to person through audio. It is capable of recognising faces and tell user whether a known person is standing in front of him or not.

A tool to download and format MS COCO dataset for multilabel classification

Drawing and visualizing bounding boxes and key points.

Intelligent Advertisement Generation for e-commerce websites using deep learning.

Deep learning project that generates natural-language captions for images using CNN for visual feature extraction and LSTM for text generation. Built with TensorFlow/Keras and trained on the MS COCO dataset for AI-powered image understanding.