Zobrazeno 1 - 10
of 496
pro vyhledávání: '"Zhao mengjie"'
Current keyword decision-making in sponsored search advertising relies on large, static datasets, limiting the ability to automatically set up keywords and adapt to real-time KPI metrics and product updates that are essential for effective advertisin
Externí odkaz:
http://arxiv.org/abs/2412.03577
Autor:
Zhao, Mengjie, Zhong, Zhi, Mao, Zhuoyuan, Yang, Shiqi, Liao, Wei-Hsiang, Takahashi, Shusuke, Wakaki, Hiromi, Mitsufuji, Yuki
We present OpenMU-Bench, a large-scale benchmark suite for addressing the data scarcity issue in training multimodal language models to understand music. To construct OpenMU-Bench, we leveraged existing datasets and bootstrapped new annotations. Open
Externí odkaz:
http://arxiv.org/abs/2410.15573
Autor:
Mirza, M. Jehanzeb, Zhao, Mengjie, Mao, Zhuoyuan, Doveh, Sivan, Lin, Wei, Gavrikov, Paul, Dorkenwald, Michael, Yang, Shiqi, Jha, Saurav, Wakaki, Hiromi, Mitsufuji, Yuki, Possegger, Horst, Feris, Rogerio, Karlinsky, Leonid, Glass, James
In this work, we propose a novel method (GLOV) enabling Large Language Models (LLMs) to act as implicit Optimizers for Vision-Langugage Models (VLMs) to enhance downstream vision tasks. Our GLOV meta-prompts an LLM with the downstream task descriptio
Externí odkaz:
http://arxiv.org/abs/2410.06154
Autor:
Jha, Saurav, Yang, Shiqi, Ishii, Masato, Zhao, Mengjie, Simon, Christian, Mirza, Muhammad Jehanzeb, Gong, Dong, Yao, Lina, Takahashi, Shusuke, Mitsufuji, Yuki
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple
Externí odkaz:
http://arxiv.org/abs/2410.00700
Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. However, relying solely on physical sensors can be limited due to their cost, placement constraints, or inability to directly measure certain criti
Externí odkaz:
http://arxiv.org/abs/2407.18691
Autor:
Comunità, Marco, Zhong, Zhi, Takahashi, Akira, Yang, Shiqi, Zhao, Mengjie, Saito, Koichi, Ikemiya, Yukara, Shibuya, Takashi, Takahashi, Shusuke, Mitsufuji, Yuki
Recent advances in generative models that iteratively synthesize audio clips sparked great success to text-to-audio synthesis (TTA), but with the cost of slow synthesis speed and heavy computation. Although there have been attempts to accelerate the
Externí odkaz:
http://arxiv.org/abs/2406.17672
Autor:
Wakaki, Hiromi, Mitsufuji, Yuki, Maeda, Yoshinori, Nishimura, Yukiko, Gao, Silin, Zhao, Mengjie, Yamada, Keiichi, Bosselut, Antoine
We propose a new benchmark, ComperDial, which facilitates the training and evaluation of evaluation metrics for open-domain dialogue systems. ComperDial consists of human-scored responses for 10,395 dialogue turns in 1,485 conversations collected fro
Externí odkaz:
http://arxiv.org/abs/2406.11228
Autor:
Yang, Shiqi, Zhong, Zhi, Zhao, Mengjie, Takahashi, Shusuke, Ishii, Masato, Shibuya, Takashi, Mitsufuji, Yuki
In recent years, with the realistic generation results and a wide range of personalized applications, diffusion-based generative models gain huge attention in both visual and audio generation areas. Compared to the considerable advancements of text2i
Externí odkaz:
http://arxiv.org/abs/2405.14598
Publikováno v:
Vol. 8 No. 1 (2024): Proceedings of the European Conference of the PHM Society 2024 Technical Papers
Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While bearing sensors are typically placed on the bearing housing, direct load mo
Externí odkaz:
http://arxiv.org/abs/2404.02304
Autor:
Xie, Zhouhang, Majumder, Bodhisattwa Prasad, Zhao, Mengjie, Maeda, Yoshinori, Yamada, Keiichi, Wakaki, Hiromi, McAuley, Julian
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. Addressing such a task requires a system that can infer \textit{how} to motivate a user effectively. We propose
Externí odkaz:
http://arxiv.org/abs/2403.15737