Soft attention mechanism for video caption generation
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Updated
Jul 17, 2017 - Python
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
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.
Summaries and notes on Deep Learning research papers in natural language processing(NLP) domain.
Using Unilm-Chinese to generate Chinese couplets. 使用unilm中文版来生成中文对联。
This project uses an Encoder-Decoder architecture with Attention to generate descriptive captions for images using InceptionV3 for feature extraction and LSTM for decoding. Part of AI825 Visual Recognition course taught by Prof. Dinesh Babu Jayagopi and Prof. Vishwanath G, IIITB
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