A paper list of some recent Transformer-based CV works.

Awesome Transformers (self-attention) in Computer Vision

MedViT: A Robust Vision Transformer for Generalized Medical Image Classification (Computers in Biology and Medicine 2023)

This repository contains a curated list of free and high-quality resources for learning various topics in artificial intelligence, including deep learning, natural language processing, computer vision, reinforcement learning, MLOps, multimodal machine learning, transformers, and prompt engineering.

Semantic segmentation is an important job in computer vision, and its applications have grown in popularity over the last decade.We grouped the publications that used various forms of segmentation in this repository. Particularly, every paper is built on a transformer.

Implementation of Image Classification using Visual Transformers in Amazon SageMaker based on the ideas from research paper - Visual Transformers: Token-based Image Representation and Processing for Computer Vision.

An implementation of the Visual Transformer Architecture introduced in the paper "Visual Transformers: Token-based Image Representation and Processing for Computer Vision" by Wu et al.

An experimental research framework in PyTorch for adapting the Baby Dragon Hatchling (BDH) architecture to computer vision tasks using a Vision Transformer (ViT) approach.

Computer Vision project focused on detecting smoke and fire in wild environments. The Google Vision Transformer was fine-tuned on a custom dataset.

This repo contains transformers model from scratch. A transformer is a deep learning model that adopts the mechanism of self-attention, differentially weighting the significance of each part of the input data. It is used primarily in the fields of natural language processing and computer vision.

This repository contains the implementation of Depth Prediction Transformers (DPT), a deep learning model for accurate depth estimation in computer vision tasks. DPT leverages the transformer architecture and an encoder-decoder framework to capture fine-grained details, model long-range dependencies, and generate precise depth predictions.

this repo is for linkedin learning course: Hands-On Introduction to Transformers for Computer Vision

n recent years the NLP community has seen many breakthoughs in Natural Language Processing, especially the shift to transfer learning. Models like ELMo, fast.ai's ULMFiT, Transformer and OpenAI's GPT have allowed researchers to achieves state-of-the-art results on multiple benchmarks and provided the community with large pre-trained models with high performance. This shift in NLP is seen as NLP's ImageNet moment, a shift in computer vision a few year ago when lower layers of deep learning networks with million of parameters trained on a specific task can be reused and fine-tuned for other tasks, rather than training new networks from scratch. One of the most biggest milestones in the evolution of NLP recently is the release of Google's BERT, which is described as the beginning of a new era in NLP. In this notebook I'll use the HuggingFace's `transformers` library to fine-tune pretrained BERT model for a classification task. Then I will compare the BERT's performance with a baseline model, in which I use a TF-IDF vectorizer and a Naive Bayes classifier. The `transformers` library help us quickly and efficiently fine-tune the state-of-the-art BERT model and yield an accuracy rate **10%** higher than the baseline model.

SET-ViT: Spectral-Enhanced Vision Transformer for Tiny/Small Object Detection Tiny objects are difficult for computer vision models because background noise overwhelms the small visual details. Even modern Vision Transformers often miss tiny targets in aerial imagery, traffic scenes, or surveillance videos.

Photogrammetric Coordinate System Transformer, in short PCST, is a python based GUI program, that intends to help photogrammetrist and computer vision analyst, rapidly pick the best model on their data

In the midst of emerging technology, there was particularly one specific machine learning model that caught the attention of researchers: ’Transformers’. They are attention based model which was able to beat the state-of-the-art model for computer vision tasks but it is still unexplored properly in Medical domains. Due to which in this projects we are mainly focusing on the use of transformer as a binary classification model for Chest X-ray images. Throughout this project we will be going more in-depth about the architecture of transformers and their implementation in computer vision task.However, this project does not focus on producing a new findings, rather, on testing the performance of transformers for chest X-ray images compared to different State-of-the-art Convolution Neural Networks (CNNs), Deep Neural Networks(DNN) models.

About this Specialization The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. Along the way, you will get career advice from deep learning experts from industry and academia.

Guardian Vision is a computer vision-based system for real-time anomaly detection in surveillance videos. It combines I3D and Vision Transformer (ViT) models to identify unusual human behavior with high accuracy.

A complete machine learning pipeline for automated cow behavior classification using computer vision. This project combines YOLO object detection with Vision Transformer (ViT) classification to analyze cow behaviors in video footage.

Implementation of different Transformer architectures for vision tasks such as ViT and Swin Transformer

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The three tools that are being looked at are YOLO(You Only Look Once), DETR (DEtection TRansformers), and ViT (Vision Transformer). These are various deep learning models and architectures used in computer vision and object detection tasks. The idea is to see how these tools can be used to optimize weapon detection.

A repository for a vision transformer based project for the Master Degree in Computer Engineering.

A transformer-based plant disease diagnostic tool via image analysis and computer vision in leaf snapshots.

In this repo, I will bring some explanations, descriptions, and examples of the application of transformers for computer vision.

Style Transfer on images using Transformers. Project done as a part of the 'Computer Vision' course in IIIT Hyderabad (Spring 2024)

Implementation of Novel Object Detection System for Computer Night Vision Images using Residual 3D Transformer-based YoloV8 with Adaptive GRU in Edge and Cloud Sector

A robust, intelligent traffic monitoring system built to detect, track, and analyze vehicle movement in diverse conditions using advanced computer vision and transformer-based AI models.

Explore AI-powered image analysis with this interactive tool. Features real-time image captioning and object detection using Python, Streamlit, and Transformers. Ideal for AI enthusiasts and developers interested in computer vision.