论文标题
部分可观测时空混沌系统的无模型预测
Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety
论文作者
论文摘要
基于深度学习的模型，用于图像字幕和低光图像增强的模型取得了惊人的进步。本文首次开发了一种深度学习模型，该模型将夜面场景转化为句子，为视障女性安全性的AI应用开辟了新的可能性。受图像字幕和视觉问题回答的启发，开发了一种新颖的交互式图像字幕。用户可以通过影响注意力评分来使AI专注于任何感兴趣的人。注意上下文向量是从CNN功能向量和用户提供的开始单词计算的。编码器注意事项神经网络学会从低亮度图像中产生字幕。本文展示了如何通过在交互式视觉模型中研究新颖的AI能力来实现女性安全，以在夜晚感知环境。
There is amazing progress in Deep Learning based models for Image captioning and Low Light image enhancement. For the first time in literature, this paper develops a Deep Learning model that translates night scenes to sentences, opening new possibilities for AI applications in the safety of visually impaired women. Inspired by Image Captioning and Visual Question Answering, a novel Interactive Image Captioning is developed. A user can make the AI focus on any chosen person of interest by influencing the attention scoring. Attention context vectors are computed from CNN feature vectors and user-provided start word. The Encoder-Attention-Decoder neural network learns to produce captions from low brightness images. This paper demonstrates how women safety can be enabled by researching a novel AI capability in the Interactive Vision-Language model for perception of the environment in the night.
