Computer vision

Graiphic

Vake AS
Quality control on production lines with Computer Vision and TinyML
This repository includes a jupyter notebook that presents a complete pipeline for: EDA on image data Data preparation and augmentation Deep learning (CNN) models development for image classification with TensorFlow Models evaluation Model interpret...

Computer Vision
Our dream to make machines sense and perceive (notably see) comes true: nowadays Computer Vision enables diverse applications: - Autonomous Systems (cars, drones, vessels) Perception, - Robotics Perception and Control, - Intelligent Human-Machine Int...

Social Impact of AI Science and Engineering: Information Filtering and Disinformation
Our world is increasingly complex, in terms of both its material components (e.g., smart cities, infrastructure) and its social processes (e.g., social media outreach). Both individual humans and entire societies find it difficult to cope with world compl...
Few-Shot Object Detection (FsDet) - Training tools for custom data
This is a fork of Few-shot Object Detection (FsDet) method, adding an easy to use tool for training on custom datasets

AI Science and Engineering: A new scientific discipline?
Is “AI Science and Engineering” an un upcoming scientific discipline that can fuse AI, brain and mind studies and social engineering? As Artificial Intelligence (AI) studies and research flourish worldwide, it is worth debating whether we already observe ...
Playable Video Generation
Novel framework for Playable Video Generation that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to...
IEP-GAN: Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer
PyTorch implementation of Intrinsic-Extrinsic Preserved Generative Adversarial Network (IEP-GAN) for both intrinsic (i.e., shape) and extrinsic (i.e., pose) information preservation. Extrinsically, a co-occurrence discriminator is used to capture the stru...
Efficient Training of Visual Transformers with Small Datasets
A tool to allow Visual Transformers (VTs) to learn spatial relations within an image making the VT training much more robust when training data is scarce. The tool can be used jointly with the standard (supervised) training and it does not depend on speci...
Cycle-In-Cycle GANs
Python implementation of novel Cycle In Cycle Generative Adversarial Network (C2GAN) for the task of keypoint-guided image generation. The C2GAN is a cross-modal framework exploring a joint exploitation of the keypoint and the image data in an interactive...