Several services for people with visual disabilities have emerged recently
due to achievements in Assistive Technologies and Artificial Intelligence
areas. Despite the growth in assistive systems availability, there is a lack of
services that support specific tasks, such as understanding the image context
presented in online content, e.g., webinars. Image captioning techniques and
their variants are limited as Assistive Technologies as they do not match the
needs of visually impaired people when generating specific descriptions. We
propose an approach for generating context of webinar images combining a dense
captioning technique with a set of filters, to fit the captions in our domain,
and a language model for the abstractive summary task. The results demonstrated
that we can produce descriptions with higher interpretability and focused on
the relevant information for that group of people by combining image analysis
methods and neural language models.

本研究提出一种结合图像分析方法和神经语言模型的方法，结合密集字幕技术和一组筛选器生成网络研讨会图像上下文信息，以生成高度可解释且聚焦于相关信息的描述，以应对视觉障碍个体面临的信息获取问题。

为视力受损者描述图像的认知和视觉细节：生成包容段落的方法

Describing image focused in cognitive and visual details for visually impaired people: An approach to generating inclusive paragraphs

Applications such as textual entailment, plagiarism detection or document
clustering rely on the notion of semantic similarity, and are usually
approached with dimension reduction techniques like LDA or with embedding-based
neural approaches. We present a scenario where semantic similarity is not
enough, and we devise a neural approach to learn semantic relatedness. The
scenario is text spotting in the wild, where a text in an image (e.g. street
sign, advertisement or bus destination) must be identified and recognized. Our
goal is to improve the performance of vision systems by leveraging semantic
information. Our rationale is that the text to be spotted is often related to
the image context in which it appears (word pairs such as Delta-airplane, or
quarters-parking are not similar, but are clearly related). We show how
learning a word-to-word or word-to-sentence relatedness score can improve the
performance of text spotting systems up to 2.9 points, outperforming other
measures in a benchmark dataset.

我们提出了一种神经方法来学习语义相关性，并通过学习单词对句子或单词到单词的关系分数来改善文本识别性能，证明了在识别图像中的文本时，语义相关性可以优于其他度量标准。