Zobrazeno 1 - 10
of 229
pro vyhledávání: '"Ma, Zhengyu"'
Seeking high-quality neural latent representations to reveal the intrinsic correlation between neural activity and behavior or sensory stimulation has attracted much interest. Currently, some deep latent variable models rely on behavioral information
Externí odkaz:
http://arxiv.org/abs/2408.07908
Autor:
Yu, Liutao, Huang, Liwei, Zhou, Chenlin, Zhang, Han, Ma, Zhengyu, Zhou, Huihui, Tian, Yonghong
Video action recognition (VAR) plays crucial roles in various domains such as surveillance, healthcare, and industrial automation, making it highly significant for the society. Consequently, it has long been a research spot in the computer vision fie
Externí odkaz:
http://arxiv.org/abs/2406.15034
Autor:
Ren, Tianhe, Jiang, Qing, Liu, Shilong, Zeng, Zhaoyang, Liu, Wenlong, Gao, Han, Huang, Hongjie, Ma, Zhengyu, Jiang, Xiaoke, Chen, Yihao, Xiong, Yuda, Zhang, Hao, Li, Feng, Tang, Peijun, Yu, Kent, Zhang, Lei
This paper introduces Grounding DINO 1.5, a suite of advanced open-set object detection models developed by IDEA Research, which aims to advance the "Edge" of open-set object detection. The suite encompasses two models: Grounding DINO 1.5 Pro, a high
Externí odkaz:
http://arxiv.org/abs/2405.10300
Autor:
Zhou, Chenlin, Zhang, Han, Yu, Liutao, Ye, Yumin, Zhou, Zhaokun, Huang, Liwei, Ma, Zhengyu, Fan, Xiaopeng, Zhou, Huihui, Tian, Yonghong
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training alg
Externí odkaz:
http://arxiv.org/abs/2405.04289
Autor:
Zhou, Chenlin, Zhang, Han, Zhou, Zhaokun, Yu, Liutao, Huang, Liwei, Fan, Xiaopeng, Yuan, Li, Ma, Zhengyu, Zhou, Huihui, Tian, Yonghong
Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for energy efficiency and high performance. However, existing models in this domain still
Externí odkaz:
http://arxiv.org/abs/2403.16552
Publikováno v:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, Volume 1, pages 7278-7292, August 2024, Bangkok, Thailand
Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs). However, EEG-based language decoding is still in its nascent stages, facing sev
Externí odkaz:
http://arxiv.org/abs/2402.17433
In the real world, visual stimuli received by the biological visual system are predominantly dynamic rather than static. A better understanding of how the visual cortex represents movie stimuli could provide deeper insight into the information proces
Externí odkaz:
http://arxiv.org/abs/2306.01354
Autor:
Che, Kaiwei, Zhou, Zhaokun, Ma, Zhengyu, Fang, Wei, Chen, Yanqi, Shen, Shuaijie, Yuan, Li, Tian, Yonghong
The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties. Recent advancements in SNN architecture, such as
Externí odkaz:
http://arxiv.org/abs/2306.00807
Autor:
Qiu, Haonan, Song, Zeyin, Chen, Yanqi, Ning, Munan, Fang, Wei, Sun, Tao, Ma, Zhengyu, Yuan, Li, Tian, Yonghong
Biologically inspired spiking neural networks (SNNs) have garnered considerable attention due to their low-energy consumption and spatio-temporal information processing capabilities. Most existing SNNs training methods first integrate output informat
Externí odkaz:
http://arxiv.org/abs/2305.13909
Autor:
Zhou, Chenlin, Zhang, Han, Zhou, Zhaokun, Yu, Liutao, Ma, Zhengyu, Zhou, Huihui, Fan, Xiaopeng, Tian, Yonghong
Deep spiking neural networks (SNNs) have drawn much attention in recent years because of their low power consumption, biological rationality and event-driven property. However, state-of-the-art deep SNNs (including Spikformer and Spikingformer) suffe
Externí odkaz:
http://arxiv.org/abs/2305.05954