Interactive Attention AI to translate low light photos to captions for night scene understanding in women safety
This work addresses safety concerns for visually impaired women by enabling AI-assisted perception in low-light conditions, though it appears incremental as it combines existing image captioning and attention mechanisms.
The paper tackles the problem of generating captions from low-light images to aid night scene understanding, specifically for women's safety, by developing a deep learning model that translates night scenes to sentences with user-influenced attention.
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.