Zobrazeno 1 - 10
of 29
pro vyhledávání: '"KITADA, Shunsuke"'
Large language models (LLMs) have proven effective for layout generation due to their ability to produce structure-description languages, such as HTML or JSON, even without access to visual information. Recently, LLM providers have evolved these mode
Externí odkaz:
http://arxiv.org/abs/2412.04237
Layout generation is a task to synthesize a harmonious layout with elements characterized by attributes such as category, position, and size. Human designers experiment with the placement and modification of elements to create aesthetic layouts, howe
Externí odkaz:
http://arxiv.org/abs/2409.16689
Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail distribution, featuring numer
Externí odkaz:
http://arxiv.org/abs/2401.11431
Autor:
Kitada, Shunsuke
With the dramatic advances in deep learning technology, machine learning research is focusing on improving the interpretability of model predictions as well as prediction performance in both basic and applied research. While deep learning models have
Externí odkaz:
http://arxiv.org/abs/2303.14116
We propose a simple yet effective image captioning framework that can determine the quality of an image and notify the user of the reasons for any flaws in the image. Our framework first determines the quality of images and then generates captions us
Externí odkaz:
http://arxiv.org/abs/2211.09427
Publikováno v:
in IEEE Access, vol. 10, pp. 120023-120034, 2022
There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combi
Externí odkaz:
http://arxiv.org/abs/2209.03126
Publikováno v:
Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM'22), October 17--21, 2022, Atlanta, GA, USA
It is often difficult to correctly infer a writer's emotion from text exchanged online, and differences in recognition between writers and readers can be problematic. In this paper, we propose a new framework for detecting sentences that create diffe
Externí odkaz:
http://arxiv.org/abs/2208.14244
Publikováno v:
Appl. Sci. 2022, 12(7), 3594
Discontinuing ad creatives at an appropriate time is one of the most important ad operations that can have a significant impact on sales. Such operational support for ineffective ads has been less explored than that for effective ads. After pre-analy
Externí odkaz:
http://arxiv.org/abs/2204.11588
Autor:
Kitada, Shunsuke, Iyatomi, Hitoshi
Publikováno v:
Applied Intelligence, Springer, 2022
Although attention mechanisms have become fundamental components of deep learning models, they are vulnerable to perturbations, which may degrade the prediction performance and model interpretability. Adversarial training (AT) for attention mechanism
Externí odkaz:
http://arxiv.org/abs/2104.08763
We propose a new character-based text classification framework for non-alphabetic languages, such as Chinese and Japanese. Our framework consists of a variational character encoder (VCE) and character-level text classifier. The VCE is composed of a $
Externí odkaz:
http://arxiv.org/abs/2011.04184