Product Title Generation From Image using Semantic Compositional Network and Top-Down Attention Model
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Updated
May 31, 2020 - Jupyter Notebook
Product Title Generation From Image using Semantic Compositional Network and Top-Down Attention Model
Beginners' try with natural-language-processing end-to-end projects.
This repository hosts the scripts and some of the pre-trained models presented in out paper "ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network", IEEE Access, 2022.
Graph Attention Networks (GATs) for node classification and regression tasks
Soft attention mechanism for video caption generation
Audio and Music Synthesis with Machine Learning
Mutex attention network for COVID-19 diagnosis
Pytorch Implement of diffusion model
Official repository of the paper "Zero-shot face recognition: Improving the discriminability of visual face features using a Semantic-Guided Attention Model", Expert Systems With Applications 2022
The primary goal was to develop a deep learning model capable of generating descriptive captions for images, empowering visually impaired individuals to perceive visual content through auditory means.
Implementation of SelfExtend from the paper "LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning" from Pytorch and Zeta
Research on Image Compression using Deep Neural Networks
Attention based CNN
A TensorFlow implementation of the Transformer model for machine translation tasks. This package includes data loading, model definition, and training scripts for translating Portuguese to English using the TED Talks dataset. The repository provides a complete pipeline from preprocessing the data to training and testing the model.
Sentiment Analysis on Tweets based on Recurrent Neural Network and attention model, implemented using Tensorflow library.
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