Key points are not available for this paper at this time.
Abstract 83% of the world's population owned a smartphone today. The use of smartphones as personal assistants is also emerging. This article proposes a new video dataset suitable for few-shot or zero-shot learning. The dataset contains handheld product videos captured using a handheld smartphone by visually impaired (VI) people. With the ultimate goal of improving assistive technology for the VI, the dataset is designed to facilitate question-answering based on both textual and visual features. One of the objectives of such video analytics is to develop assistive technology for visually impaired people for day-to-day activity management and also provide an independent shopping experience. This article highlights the limitations of existing deep learning-based approaches when applied to the dataset, suggesting that they pose novel challenges for computer vision researchers. We propose a zero-shot VQA for the problem. Despite the current approaches' poor performance, they foster a training-free zero-shot approach, providing a baseline for visual question-answering towards the foundation for future research. We believe the dataset provides new challenges and attracts many computer vision researchers. This dataset will be available.
Building similarity graph...
Analyzing shared references across papers
Loading...
Ratnabali Pal
Samarjit Kar
Arif Ahmed Sekh
National Institute of Technology Durgapur
Xavier University
Building similarity graph...
Analyzing shared references across papers
Loading...
Pal et al. (Fri,) studied this question.
www.synapsesocial.com/papers/68e58a50b6db643587525fe0 — DOI: https://doi.org/10.21203/rs.3.rs-4549605/v1