Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Hinami, Ryota"'
Recognizing characters and predicting speakers of dialogue are critical for comic processing tasks, such as voice generation or translation. However, because characters vary by comic title, supervised learning approaches like training character class
Externí odkaz:
http://arxiv.org/abs/2404.13993
Autor:
Shimizu, Yugo, Furuta, Ryosuke, Ouyang, Delong, Taniguchi, Yukinobu, Hinami, Ryota, Ishiwatari, Shonosuke
Japanese comics (called manga) are traditionally created in monochrome format. In recent years, in addition to monochrome comics, full color comics, a more attractive medium, have appeared. Unfortunately, color comics require manual colorization, whi
Externí odkaz:
http://arxiv.org/abs/2107.07943
We tackle the problem of machine translation of manga, Japanese comics. Manga translation involves two important problems in machine translation: context-aware and multimodal translation. Since text and images are mixed up in an unstructured fashion
Externí odkaz:
http://arxiv.org/abs/2012.14271
Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years. A downside to diffusion is that it performs slowly in comparison to the naiv
Externí odkaz:
http://arxiv.org/abs/1811.10907
Existing approximate nearest neighbor search systems suffer from two fundamental problems that are of practical importance but have not received sufficient attention from the research community. First, although existing systems perform well for the w
Externí odkaz:
http://arxiv.org/abs/1808.03969
We tackle the problem of learning concept classifiers from videos on the web without using manually labeled data. Although metadata attached to videos (e.g., video titles, descriptions) can be of help collecting training data for the target concept,
Externí odkaz:
http://arxiv.org/abs/1804.06057
In this work we propose tracking as a generic addition to the instance search task. From video data perspective, much information that can be used is not taken into account in the traditional instance search approach. This work aims to provide insigh
Externí odkaz:
http://arxiv.org/abs/1803.00479
Autor:
Hinami, Ryota, Satoh, Shin'ichi
Thanks to the success of object detection technology, we can retrieve objects of the specified classes even from huge image collections. However, the current state-of-the-art object detectors (such as Faster R-CNN) can only handle pre-specified class
Externí odkaz:
http://arxiv.org/abs/1711.09509
Region-based image retrieval (RBIR) technique is revisited. In early attempts at RBIR in the late 90s, researchers found many ways to specify region-based queries and spatial relationships; however, the way to characterize the regions, such as by usi
Externí odkaz:
http://arxiv.org/abs/1709.09106
This paper addresses the problem of joint detection and recounting of abnormal events in videos. Recounting of abnormal events, i.e., explaining why they are judged to be abnormal, is an unexplored but critical task in video surveillance, because it
Externí odkaz:
http://arxiv.org/abs/1709.09121