Anime, as a popular medium, has attracted hundreds of millions audience, especially the young followers. In recent years, various anime-related AI tasks like anime character classification (ACC), retrieval (ACR), tag-prediction (ACTP), question answering (ACQA), and generation (ACG) have been proposed to meet the requirements of various applications. However, there is still a lack of large-scale datasets for these tasks. Such a situation is definitely not beneficial to anime-related academic research and industrial applications. In this paper, to boost anime-related AI technical research and application development, we present Anime-2026, a new and large-scale anime character dataset, which can support various anime-related AI tasks, including ACC, ACR, ACTP, ACQA and ACG etc. Anime-2026 consists of 1.5M anime character images, 14k different characters, 16k unique semantic keyword tags, 10k question-answer pairs, and 4k manually designed text queries by crowdsourcing for the ACR and ACG tasks. Furthermore, to assess the dataset, we re-implement a number of generic and anime-specific AI baseline models, and conduct extensive experiments to evaluate these models on Anime-2026. In summary, as a general benchmark dataset, Anime-2026 provides the largest free anime character resource to support future anime-related AI research and development. We expect that Anime-2026 will promote the R&D of new and more advanced models and methods of various anime-related AI tasks. The dataset is available on https://huggingface.co/datasets/miaojiemiao/Anime-2026.

There are already some text-based visual question answering (TextVQA) benchmarks for developing machine's ability to answer questions based on texts in images in recent years. However, models developed on these benchmarks cannot work effectively in many real-life scenarios (e.g. traffic monitoring, shopping ads and e-learning videos) where temporal reasoning ability is required. To this end, we propose a new task named Video Text Visual Question Answering (ViteVQA in short) that aims at answering questions by reasoning texts and visual information spatiotemporally in a given video. In particular, on the one hand, we build the first ViteVQA benchmark dataset named M4-ViteVQA --- the abbreviation of Multi-category Multi-frame Multi-resolution Multi-modal benchmark for ViteVQA, which contains 7,620 video clips of 9 categories (i.e., shopping, traveling, driving, vlog, sport, advertisement, movie, game and talking) and 3 kinds of resolutions (i.e., 720p, 1080p and 1176x664), and 25,123 question-answer pairs. On the other hand, we develop a baseline method named T5-ViteVQA for the ViteVQA task. T5-ViteVQA consists of five transformers. It first extracts optical character recognition (OCR) tokens, question features, and video representations via two OCR transformers, one language transformer and one video-language transformer, respectively. Then, a multimodal fusion transformer and an answer generation module are applied to fuse multimodal information and generate the final prediction. Extensive experiments on M4-ViteVQA demonstrate the superiority of T5-ViteVQA to the existing approaches of TextVQA and VQA tasks. The ViteVQA benchmark is available in https://github.com/bytedance/VTVQA.