Zobrazeno 1 - 10
of 227
pro vyhledávání: '"HASSANI, ALI"'
Autor:
Hassani, Ali
Safety risk evaluation is critical in autonomous vehicle applications. This research aims to develop, implement, and validate new safety monitoring methods for navigation in Global Navigation Satellite System (GNSS)-denied environments. The methods q
Externí odkaz:
http://hdl.handle.net/10919/115298
Neighborhood attention reduces the cost of self attention by restricting each token's attention span to its nearest neighbors. This restriction, parameterized by a window size and dilation factor, draws a spectrum of possible attention patterns betwe
Externí odkaz:
http://arxiv.org/abs/2403.04690
Autor:
Liu, Feng, Ashbaugh, Ryan, Chimitt, Nicholas, Hassan, Najmul, Hassani, Ali, Jaiswal, Ajay, Kim, Minchul, Mao, Zhiyuan, Perry, Christopher, Ren, Zhiyuan, Su, Yiyang, Varghaei, Pegah, Wang, Kai, Zhang, Xingguang, Chan, Stanley, Ross, Arun, Shi, Humphrey, Wang, Zhangyang, Jain, Anil, Liu, Xiaoming
Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to-end design, development and evaluation of FarSight, an innovative sof
Externí odkaz:
http://arxiv.org/abs/2306.17206
Universal Image Segmentation is not a new concept. Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not
Externí odkaz:
http://arxiv.org/abs/2211.06220
Image generation has been a long sought-after but challenging task, and performing the generation task in an efficient manner is similarly difficult. Often researchers attempt to create a "one size fits all" generator, where there are few differences
Externí odkaz:
http://arxiv.org/abs/2211.05770
Autor:
Hassani, Ali, Shi, Humphrey
Transformers are quickly becoming one of the most heavily applied deep learning architectures across modalities, domains, and tasks. In vision, on top of ongoing efforts into plain transformers, hierarchical transformers have also gained significant
Externí odkaz:
http://arxiv.org/abs/2209.15001
Autor:
Wang, Yulin, Yue, Yang, Xu, Xinhong, Hassani, Ali, Kulikov, Victor, Orlov, Nikita, Song, Shiji, Shi, Humphrey, Huang, Gao
Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e.g., allocating the majority of computation to a task-relevant subset of frames or the most valuable im
Externí odkaz:
http://arxiv.org/abs/2209.13465
This paper demonstrates a novel approach to improve face-recognition pose-invariance using semantic-segmentation features. The proposed Seg-Distilled-ID network jointly learns identification and semantic-segmentation tasks, where the segmentation tas
Externí odkaz:
http://arxiv.org/abs/2209.01115
Despite the popularity of Model Compression and Multitask Learning, how to effectively compress a multitask model has been less thoroughly analyzed due to the challenging entanglement of tasks in the parameter space. In this paper, we propose DiSpars
Externí odkaz:
http://arxiv.org/abs/2206.04662
We present Neighborhood Attention (NA), the first efficient and scalable sliding-window attention mechanism for vision. NA is a pixel-wise operation, localizing self attention (SA) to the nearest neighboring pixels, and therefore enjoys a linear time
Externí odkaz:
http://arxiv.org/abs/2204.07143