The paper "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention" introduces an attention-based model for generating captions of images. The authors, Kelvin Xu and others, draw inspiration from recent advancements in machine translation and object detection to develop a model that can automatically learn to describe image content. The model is trained using both deterministic backpropagation and stochastic variational methods. It demonstrates the ability to focus on salient objects in the image while generating captions, as evidenced by visualizations of the model's attention mechanism. The paper evaluates the model on three benchmark datasets: Flickr8k, Flickr30k, and MS COCO, achieving state-of-the-art performance. The contributions of the paper include the introduction of two attention-based caption generators, the visualization of attention mechanisms, and the validation of the usefulness of attention in caption generation. The authors also discuss related work and provide a detailed description of the model's architecture, including the encoder and decoder components. The paper concludes by highlighting the interpretability and effectiveness of the proposed attention-based approach.The paper "Show, Attend and Tell: Neural Image Caption Generation with Visual Attention" introduces an attention-based model for generating captions of images. The authors, Kelvin Xu and others, draw inspiration from recent advancements in machine translation and object detection to develop a model that can automatically learn to describe image content. The model is trained using both deterministic backpropagation and stochastic variational methods. It demonstrates the ability to focus on salient objects in the image while generating captions, as evidenced by visualizations of the model's attention mechanism. The paper evaluates the model on three benchmark datasets: Flickr8k, Flickr30k, and MS COCO, achieving state-of-the-art performance. The contributions of the paper include the introduction of two attention-based caption generators, the visualization of attention mechanisms, and the validation of the usefulness of attention in caption generation. The authors also discuss related work and provide a detailed description of the model's architecture, including the encoder and decoder components. The paper concludes by highlighting the interpretability and effectiveness of the proposed attention-based approach.