Zobrazeno 1 - 10
of 9 906
pro vyhledávání: '"Nagahara A"'
Autor:
Li, Mengmou, Nagahara, Masaaki
We show that global exponential convergence for the augmented primal-dual gradient algorithms can be achieved for partially strongly convex functions. In particular, the objective function only needs to be strongly convex in the subspace satisfying t
Externí odkaz:
http://arxiv.org/abs/2410.02192
Autor:
Wang, Bowen, Chang, Jiuyang, Qian, Yiming, Chen, Guoxin, Chen, Junhao, Jiang, Zhouqiang, Zhang, Jiahao, Nakashima, Yuta, Nagahara, Hajime
Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GPT-4 excel in medical question answering but may face challenges in the
Externí odkaz:
http://arxiv.org/abs/2408.01933
Facial expression spotting, identifying periods where facial expressions occur in a video, is a significant yet challenging task in facial expression analysis. The issues of irrelevant facial movements and the challenge of detecting subtle motions in
Externí odkaz:
http://arxiv.org/abs/2407.20799
In this work, we propose a novel learning-based method to jointly estimate the shape and subsurface scattering (SSS) parameters of translucent objects by utilizing polarization cues. Although polarization cues have been used in various applications,
Externí odkaz:
http://arxiv.org/abs/2407.08149
The imperative to comprehend the behaviors of deep learning models is of utmost importance. In this realm, Explainable Artificial Intelligence (XAI) has emerged as a promising avenue, garnering increasing interest in recent years. Despite this, most
Externí odkaz:
http://arxiv.org/abs/2407.05616
The growing use of deep learning in safety-critical applications, such as medical imaging, has raised concerns about limited labeled data, where this demand is amplified as model complexity increases, posing hurdles for domain experts to annotate dat
Externí odkaz:
http://arxiv.org/abs/2407.02335
Facial expression spotting is a significant but challenging task in facial expression analysis. The accuracy of expression spotting is affected not only by irrelevant facial movements but also by the difficulty of perceiving subtle motions in micro-e
Externí odkaz:
http://arxiv.org/abs/2403.15994
We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Different from the conventional coded-aperture imaging method, our method applies a sequence of coding pat
Externí odkaz:
http://arxiv.org/abs/2403.07244
Autor:
Li, Chenhao, Ono, Taishi, Uemori, Takeshi, Mihara, Hajime, Gatto, Alexander, Nagahara, Hajime, Moriuchi, Yuseke
Multi-view inverse rendering is the problem of estimating the scene parameters such as shapes, materials, or illuminations from a sequence of images captured under different viewpoints. Many approaches, however, assume single light bounce and thus fa
Externí odkaz:
http://arxiv.org/abs/2311.13187
Large-scale models trained on extensive datasets, have emerged as the preferred approach due to their high generalizability across various tasks. In-context learning (ICL), a popular strategy in natural language processing, uses such models for diffe
Externí odkaz:
http://arxiv.org/abs/2311.03648