1 code implementation • 29 Feb 2024 • Nuo Xu, Wen Wang, Rong Yang, Mengjie Qin, Zheyuan Lin, Wei Song, Chunlong Zhang, Jason Gu, Chao Li
Object-goal navigation is a challenging task that requires guiding an agent to specific objects based on first-person visual observations.
no code implementations • 7 Oct 2023 • Qi Li, Jiaxin Cai, Yuanlong Yu, Jason Gu, Jia Pan, Wenxi Liu
Within the domain of UAV imagery analysis, the segmentation of ultra-high resolution images emerges as a substantial and intricate challenge, especially when grappling with the constraints imposed by GPU memory-restricted computational devices.
no code implementations • 2 Oct 2023 • Victor Gao, Issam Hammad, Kamal El-Sankary, Jason Gu
This paper presents a novel method to boost the performance of CNN inference accelerators by utilizing subtractors.
no code implementations • 27 Jul 2022 • Fiseha B. Tesema, Zheyuan Lin, Shiqiang Zhu, Wei Song, Jason Gu, Hong Wu
After fusion, one BiGRU layer is attached to model the joint temporal dynamics.
no code implementations • CVPR 2022 • Jiachen Li, Bin Wang, Shiqiang Zhu, Xin Cao, Fan Zhong, Wenxuan Chen, Te Li, Jason Gu, Xueying Qin
Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes.
no code implementations • 19 Sep 2021 • Shuang He, Xia Lu, Jason Gu, Haitong Tang, Qin Yu, Kaiyue Liu, Haozhou Ding, Chunqi Chang, Nizhuan Wang
For semantic segmentation of remote sensing images (RSI), trade-off between representation power and location accuracy is quite important.
no code implementations • 26 Dec 2019 • Issam Hammad, Kamal El-Sankary, Jason Gu
A comparison of the performance of various machine learning models to predict the direction of a wall following robot is presented in this paper.
no code implementations • 26 Dec 2019 • Issam Hammad, Kamal El-Sankary, Jason Gu
The paper demonstrates that using approximate multipliers for CNN training can significantly enhance the performance in terms of speed, power, and area at the cost of a small negative impact on the achieved accuracy.
no code implementations • 3 Jan 2019 • Weidong Zhang, Wei zhang, Jason Gu
More specifically, we present an encoder-decoder network with shared encoder and two separate decoders, which are composed of multiple deconvolution (transposed convolution) layers, to jointly learn the edge maps and semantic labels of a room image.