Zobrazeno 1 - 10
of 562
pro vyhledávání: '"Feng Feifei"'
Publikováno v:
Zhongliu Fangzhi Yanjiu, Vol 49, Iss 6, Pp 593-598 (2022)
Objective To investigate the expression of lncRNA HOTAIR, HOTAIR, CRNDE and AFAP1-AS1 in lung cancer patients with bone metastasis (LCWBM), and to elucidate the diagnostic value of lncRNAs for LCWBM. Methods Serum was collected from 38 LCWBM patients
Externí odkaz:
https://doaj.org/article/94af9e7f9866416691b7d59486ef7f09
Autor:
Wen, Junjie, Zhu, Minjie, Zhu, Yichen, Tang, Zhibin, Li, Jinming, Zhou, Zhongyi, Li, Chengmeng, Liu, Xiaoyu, Peng, Yaxin, Shen, Chaomin, Feng, Feifei
In this paper, we present DiffusionVLA, a novel framework that seamlessly combines the autoregression model with the diffusion model for learning visuomotor policy. Central to our approach is a next-token prediction objective, enabling the model to r
Externí odkaz:
http://arxiv.org/abs/2412.03293
Transformer-based Diffusion Probabilistic Models (DPMs) have shown more potential than CNN-based DPMs, yet their extensive computational requirements hinder widespread practical applications. To reduce the computation budget of transformer-based DPMs
Externí odkaz:
http://arxiv.org/abs/2410.23788
Autor:
Zhu, Minjie, Zhu, Yichen, Li, Jinming, Wen, Junjie, Xu, Zhiyuan, Liu, Ning, Cheng, Ran, Shen, Chaomin, Peng, Yaxin, Feng, Feifei, Tang, Jian
Diffusion Policy is a powerful technique tool for learning end-to-end visuomotor robot control. It is expected that Diffusion Policy possesses scalability, a key attribute for deep neural networks, typically suggesting that increasing model size woul
Externí odkaz:
http://arxiv.org/abs/2409.14411
Autor:
Wen, Junjie, Zhu, Yichen, Li, Jinming, Zhu, Minjie, Wu, Kun, Xu, Zhiyuan, Liu, Ning, Cheng, Ran, Shen, Chaomin, Peng, Yaxin, Feng, Feifei, Tang, Jian
Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA models face significant challenges: they are slow during inference and
Externí odkaz:
http://arxiv.org/abs/2409.12514
Autor:
Li, Jinming, Zhu, Yichen, Xu, Zhiyuan, Gu, Jindong, Zhu, Minjie, Liu, Xin, Liu, Ning, Peng, Yaxin, Feng, Feifei, Tang, Jian
It is fundamentally challenging for robots to serve as useful assistants in human environments because this requires addressing a spectrum of sub-problems across robotics, including perception, language understanding, reasoning, and planning. The rec
Externí odkaz:
http://arxiv.org/abs/2406.19693
Autor:
Zhu, Minjie, Zhu, Yichen, Liu, Xin, Liu, Ning, Xu, Zhiyuan, Shen, Chaomin, Peng, Yaxin, Ou, Zhicai, Feng, Feifei, Tang, Jian
Multimodal Large Language Models (MLLMs) have showcased impressive skills in tasks related to visual understanding and reasoning. Yet, their widespread application faces obstacles due to the high computational demands during both the training and inf
Externí odkaz:
http://arxiv.org/abs/2403.06199
Autor:
Zhu, Minjie, Zhu, Yichen, Li, Jinming, Wen, Junjie, Xu, Zhiyuan, Che, Zhengping, Shen, Chaomin, Peng, Yaxin, Liu, Dong, Feng, Feifei, Tang, Jian
The language-conditioned robotic manipulation aims to transfer natural language instructions into executable actions, from simple pick-and-place to tasks requiring intent recognition and visual reasoning. Inspired by the dual process theory in cognit
Externí odkaz:
http://arxiv.org/abs/2401.04181
Autor:
Wen, Junjie, Zhu, Yichen, Zhu, Minjie, Li, Jinming, Xu, Zhiyuan, Che, Zhengping, Shen, Chaomin, Peng, Yaxin, Liu, Dong, Feng, Feifei, Tang, Jian
Humans interpret scenes by recognizing both the identities and positions of objects in their observations. For a robot to perform tasks such as \enquote{pick and place}, understanding both what the objects are and where they are located is crucial. W
Externí odkaz:
http://arxiv.org/abs/2401.02814
Autor:
Wang, Haowen, Fan, Zhipeng, Zhao, Zhen, Che, Zhengping, Xu, Zhiyuan, Liu, Dong, Feng, Feifei, Huang, Yakun, Qiao, Xiuquan, Tang, Jian
Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape varia
Externí odkaz:
http://arxiv.org/abs/2308.02239