Name: Cristina Luna Jiménez
Type: User
Company: University of Augsburg
Bio: Post-Doc researcher at the Universidad de Granada (UGR).
Location: Human-Centered Multimedia Faculty of Applied Computer Science Universitätsstraße 6 86159 Augsburg
Cristina Luna Jiménez's Projects
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
mPLUG-Owl🦉: Modularization Empowers Large Language Models with Multimodality
NOVA is a tool for annotating and analyzing behaviours in social interactions. It supports Annotators using Machine Learning already during the coding process. Further it features both, discrete labels and continuous scores and a visuzalization of streams recorded with the SSI Framework.
Steven C. Y. Hung, Jia-Hong Lee, Timmy S. T. Wan, Chein-Hung Chen, Yi-Ming Chan and Chu-Song Chen. "Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning" 2019 ACM on International Conference on Multimedia Retrieval
My course work to reimplement a paper
QLoRA: Efficient Finetuning of Quantized LLMs
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Recurrent neural network for audio noise reduction
Sample repository to create app to handle forms i Heroku
Scenic: A Jax Library for Computer Vision Research and Beyond
SincNet is a neural architecture for efficiently processing raw audio samples.
[ICASSP 2024] Official code for Slowfast Network for Continuous Sign Language Recognition
Sign Language Translation for Instructional Videos - CVPR WiCV 2023
Speech Denoising with Deep Feature Losses
Repository accompanying the "Sign Pose-based Transformer for Word-level Sign Language Recognition" paper
Sequence prediction using recurrent neural networks(LSTM) with TensorFlow
WebGazer.js: Scalable Webcam EyeTracking Using User Interactions
Experiments and code of the popularity module presented for the conference WI-IAT20
WACV 2020 "Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison"