#PraCegoVer: A Large Dataset for Image Captioning in Portuguese
원문 초록
Automatically describing images using natural sentences is an important task to support visually impaired people's inclusion onto the Internet. It is still a big challenge that requires understanding the relation of the objects present in the image and their attributes and actions they are involved in. Then, visual interpretation methods are needed, but linguistic models are also necessary to verbally describe the semantic relations. This problem is known as Image Captioning. Although many datasets were proposed in the literature, the majority contains only English captions, whereas datasets with captions described in other languages are scarce. Recently, a movement called PraCegoVer arose on the Internet, stimulating users from social media to publish images, tag #PraCegoVer and add a short description of their content. Thus, inspired by this movement, we have proposed the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram. It is the first large dataset for image captioning in Portuguese with freely annotated images. Further, the captions in our dataset bring additional challenges to the problem: first, in contrast to popular datasets such as MS COCO Captions, #PraCegoVer has only one reference to each image; also, both mean and variance of our reference sentence length are significantly greater than those in the MS COCO Captions. These two characteristics contribute to making our dataset interesting due to the linguistic aspect and the challenges that it introduces to the image captioning problem. We publicly-share the dataset at https://github.com/gabrielsantosrv/PraCegoVer.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport
Domain Regeneration: How well do LLMs match syntactic properties of text domains?
AeroLite: Tag-Guided Lightweight Generation of Aerial Image Captions
iPIC-XAI: Improving PIC-XAI for Enhanced Image Captioning Explanation
Representation Learning through Multimodal Attention and Time-Sync Comments for Affective Video Content Analysis
Global Object Proposals for Improving Multi-Sentence Video Descriptions
토론
이 논문과 연결된 포럼 글이 아직 없습니다.