ABaldrati/CLIP4CirDemo

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Created over 2 years ago
Updated over 1 year ago

ABaldrati/CLIP4CirDemo

ABaldrati

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[CVPR 2022 - Demo Track] - Effective conditioned and composed image retrieval combining CLIP-based features

CLIP4Cir

[ACM TOMM 2023] - Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features

AugmentBrain

In AugmentBrain we investigate the performance of different data augmentation methods for the classification of Motor Imagery (MI) data using a Convolutional Neural Network tailored for EEG named EEGNet.

MT-BERT

In MT-BERT we reproduce a neural language understanding model which implements a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple NLU tasks.

dawntime

https://reddeadrecovery.gitlab.io/dawntime/ In dawntime we implement a volumetric light scattering effect based on the postprocessing technique described by Kenny Mitchell.

SupeRAuGAN

In SupeRAuGAN we implement a novel data augmentation technique tailored to Generative Adversarial Networks in order to reduce discriminator overfitting and stabilize training

facestretch

In facestretch we describe how we exploited the dlib’s facial landmarks in order to measure face deformation and perform an expression recognition task. We implemented multiple approaches, based metric learning, neural networks and geodesic distances