Zobrazeno 1 - 10
of 372
pro vyhledávání: '"WANG Weihan"'
Publikováno v:
Nanophotonics, Vol 13, Iss 26, Pp 4723-4731 (2024)
Thin-film-lithium-niobate (TFLN) photonics has attracted intensive attention and become very popular in recent years. Here, an ultra-compact TFLN on-chip dispersion compensator is proposed and realized to provide a promising solution for dispersion c
Externí odkaz:
https://doaj.org/article/e76c231589fa40ac9d3d52ce2cdd75f2
Publikováno v:
Cailiao gongcheng, Vol 52, Iss 11, Pp 1-11 (2024)
Fiber reinforced polymer composites are widely used in aerospace， automotive， shipbuilding， rail transportation， and other fields. With the development of lightweight high-speed aerospace and precision instrument automation， vibration probl
Externí odkaz:
https://doaj.org/article/ea391861d24842d7bb656ee3f8185047
Autor:
Wang Weihan
Publikováno v:
SHS Web of Conferences, Vol 200, p 01002 (2024)
With the rapid development of artificial intelligence technology, financial portfolio management has entered a new era characterized by data-driven and intelligent decision-making. Based on the customer- centered service philosophy, managers propose
Externí odkaz:
https://doaj.org/article/8c645fddb1fe4f64b39f561b83860f25
Multi-modal large language models (MLLMs) have demonstrated promising capabilities across various tasks by integrating textual and visual information to achieve visual understanding in complex scenarios. Despite the availability of several benchmarks
Externí odkaz:
http://arxiv.org/abs/2409.13730
Autor:
Yang, Zhen, Chen, Jinhao, Du, Zhengxiao, Yu, Wenmeng, Wang, Weihan, Hong, Wenyi, Jiang, Zhihuan, Xu, Bin, Tang, Jie
Large language models (LLMs) have demonstrated significant capabilities in mathematical reasoning, particularly with text-based mathematical problems. However, current multi-modal large language models (MLLMs), especially those specialized in mathema
Externí odkaz:
http://arxiv.org/abs/2409.13729
Autor:
Hong, Wenyi, Wang, Weihan, Ding, Ming, Yu, Wenmeng, Lv, Qingsong, Wang, Yan, Cheng, Yean, Huang, Shiyu, Ji, Junhui, Xue, Zhao, Zhao, Lei, Yang, Zhuoyi, Gu, Xiaotao, Zhang, Xiaohan, Feng, Guanyu, Yin, Da, Wang, Zihan, Qi, Ji, Song, Xixuan, Zhang, Peng, Liu, Debing, Xu, Bin, Li, Juanzi, Dong, Yuxiao, Tang, Jie
Beginning with VisualGLM and CogVLM, we are continuously exploring VLMs in pursuit of enhanced vision-language fusion, efficient higher-resolution architecture, and broader modalities and applications. Here we propose the CogVLM2 family, a new genera
Externí odkaz:
http://arxiv.org/abs/2408.16500
Autor:
Yang, Zhuoyi, Teng, Jiayan, Zheng, Wendi, Ding, Ming, Huang, Shiyu, Xu, Jiazheng, Yang, Yuanming, Hong, Wenyi, Zhang, Xiaohan, Feng, Guanyu, Yin, Da, Gu, Xiaotao, Zhang, Yuxuan, Wang, Weihan, Cheng, Yean, Liu, Ting, Xu, Bin, Dong, Yuxiao, Tang, Jie
We present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer, which can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixels. Previous vide
Externí odkaz:
http://arxiv.org/abs/2408.06072
Autor:
Ming, Yuhang, Xu, Minyang, Yang, Xingrui, Ye, Weicai, Wang, Weihan, Peng, Yong, Dai, Weichen, Kong, Wanzeng
Visual place recognition (VPR) is an essential component of many autonomous and augmented/virtual reality systems. It enables the systems to robustly localize themselves in large-scale environments. Existing VPR methods demonstrate attractive perform
Externí odkaz:
http://arxiv.org/abs/2407.21416
Autor:
Wang, Weihan, He, Zehai, Hong, Wenyi, Cheng, Yean, Zhang, Xiaohan, Qi, Ji, Gu, Xiaotao, Huang, Shiyu, Xu, Bin, Dong, Yuxiao, Ding, Ming, Tang, Jie
Recent progress in multimodal large language models has markedly enhanced the understanding of short videos (typically under one minute), and several evaluation datasets have emerged accordingly. However, these advancements fall short of meeting the
Externí odkaz:
http://arxiv.org/abs/2406.08035
Autor:
Ming, Yuhang, Yang, Xingrui, Wang, Weihan, Chen, Zheng, Feng, Jinglun, Xing, Yifan, Zhang, Guofeng
Publikováno v:
Engineering Applications of Artificial Intelligence, Volume 140, 15 January 2025, 109685
Neural Radiance Fields (NeRF) have emerged as a powerful paradigm for 3D scene representation, offering high-fidelity renderings and reconstructions from a set of sparse and unstructured sensor data. In the context of autonomous robotics, where perce
Externí odkaz:
http://arxiv.org/abs/2405.05526