Zobrazeno 1 - 10
of 94
pro vyhledávání: '"BARSOUM, EMAD"'
Autor:
Lu, Mingjie, Huang, Yuanxian, Liu, Ji, Huang, Xingliang, Li, Dong, Peng, Jinzhang, Tian, Lu, Barsoum, Emad
Occupancy Network has recently attracted much attention in autonomous driving. Instead of monocular 3D detection and recent bird's eye view(BEV) models predicting 3D bounding box of obstacles, Occupancy Network predicts the category of voxel in speci
Externí odkaz:
http://arxiv.org/abs/2412.07163
Autor:
Zhu, Haowei, Tang, Dehua, Liu, Ji, Lu, Mingjie, Zheng, Jintu, Peng, Jinzhang, Li, Dong, Wang, Yu, Jiang, Fan, Tian, Lu, Tiwari, Spandan, Sirasao, Ashish, Yong, Jun-Hai, Wang, Bin, Barsoum, Emad
Diffusion models have achieved remarkable progress in the field of image generation due to their outstanding capabilities. However, these models require substantial computing resources because of the multi-step denoising process during inference. Whi
Externí odkaz:
http://arxiv.org/abs/2410.16942
Autor:
Cui, Qinpeng, Liu, Yixuan, Zhang, Xinyi, Bao, Qiqi, Liao, Qingmin, Wang, Li, Lu, Tian, Liu, Zicheng, Wang, Zhongdao, Barsoum, Emad
Diffusion-based image super-resolution (SR) models have attracted substantial interest due to their powerful image restoration capabilities. However, prevailing diffusion models often struggle to strike an optimal balance between efficiency and perfo
Externí odkaz:
http://arxiv.org/abs/2409.17778
Autor:
Li, Guanchen, Zhao, Xiandong, Liu, Lian, Li, Zeping, Li, Dong, Tian, Lu, He, Jie, Sirasao, Ashish, Barsoum, Emad
Pre-trained language models (PLMs) are engineered to be robust in contextual understanding and exhibit outstanding performance in various natural language processing tasks. However, their considerable size incurs significant computational and storage
Externí odkaz:
http://arxiv.org/abs/2408.10473
Autor:
Jiang, Xuefeng, Wang, Fangyuan, Zheng, Rongzhang, Liu, Han, Huo, Yixiong, Peng, Jinzhang, Tian, Lu, Barsoum, Emad
Precise localization is of great importance for autonomous parking task since it provides service for the downstream planning and control modules, which significantly affects the system performance. For parking scenarios, dynamic lighting, sparse tex
Externí odkaz:
http://arxiv.org/abs/2407.05017
Autor:
Li, Zeping, Yang, Xinlong, Gao, Ziheng, Liu, Ji, Li, Guanchen, Liu, Zhuang, Li, Dong, Peng, Jinzhang, Tian, Lu, Barsoum, Emad
Large Language Models (LLMs) inherently use autoregressive decoding, which lacks parallelism in inference and results in significantly slow inference speed. While methods such as Medusa constructs parallelized heads, they lack adequate information in
Externí odkaz:
http://arxiv.org/abs/2406.13170
Autor:
Chen, Tianqi, Li, Zhe, Xu, Weixiang, Zhu, Zeyu, Li, Dong, Tian, Lu, Barsoum, Emad, Wang, Peisong, Cheng, Jian
Large language models (LLMs) have achieved remarkable performance on Natural Language Processing (NLP) tasks, but they are hindered by high computational costs and memory requirements. Ternarization, an extreme form of quantization, offers a solution
Externí odkaz:
http://arxiv.org/abs/2406.07177
Video Frame Interpolation (VFI) is a crucial technique in various applications such as slow-motion generation, frame rate conversion, video frame restoration etc. This paper introduces an efficient video frame interpolation framework that aims to str
Externí odkaz:
http://arxiv.org/abs/2404.11108
Autor:
Liu, Ji, Zhang, Zifeng, Lu, Mingjie, Wei, Hongyang, Li, Dong, Xie, Yile, Peng, Jinzhang, Tian, Lu, Sirasao, Ashish, Barsoum, Emad
Lane detection is a fundamental task in autonomous driving, and has achieved great progress as deep learning emerges. Previous anchor-based methods often design dense anchors, which highly depend on the training dataset and remain fixed during infere
Externí odkaz:
http://arxiv.org/abs/2404.07821
Bakgrund Upplysningar är ett ämne som ofta förekommer i den internationella redovisningsdebatten eftersom det utgör en nyckelfaktor för att förstå företagens finansiella rapporter. Det har även funnits ett intresse att förklara efterlevnads
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-41266