Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Mao, Junhua"'
Autor:
Li, Jiachen, Shi, Xinwei, Chen, Feiyu, Stroud, Jonathan, Zhang, Zhishuai, Lan, Tian, Mao, Junhua, Kang, Jeonhyung, Refaat, Khaled S., Yang, Weilong, Ie, Eugene, Li, Congcong
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersections in dense urban areas. In this work, we aim at identifying crossi
Externí odkaz:
http://arxiv.org/abs/2306.01075
Autor:
Zheng, Jingxiao, Shi, Xinwei, Gorban, Alexander, Mao, Junhua, Song, Yang, Qi, Charles R., Liu, Ting, Chari, Visesh, Cornman, Andre, Zhou, Yin, Li, Congcong, Anguelov, Dragomir
3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiD
Externí odkaz:
http://arxiv.org/abs/2112.12141
Autor:
Sun, Guang, Li, Lin, Duan, Yuanqiang, Chen, Yuqing, Gu, Quanbin, Wang, Yueming, Sun, Zhenkun, Mao, Junhua, Qian, Xiaodong, Duan, Lunbo
Publikováno v:
In Environmental Research 1 June 2024 250
Publikováno v:
CVPR 2020
Detecting pedestrians and predicting future trajectories for them are critical tasks for numerous applications, such as autonomous driving. Previous methods either treat the detection and prediction as separate tasks or simply add a trajectory regres
Externí odkaz:
http://arxiv.org/abs/2005.04255
Autor:
Li, Lin, Mao, Junhua, Tang, Wu, Sun, Guang, Gu, Quanbin, Lu, Xiaoyan, Shao, Ke, Chen, Yuqing, Duan, Lunbo
Publikováno v:
In Fuel 15 June 2023 342
In this paper, we focus on training and evaluating effective word embeddings with both text and visual information. More specifically, we introduce a large-scale dataset with 300 million sentences describing over 40 million images crawled and downloa
Externí odkaz:
http://arxiv.org/abs/1611.08321
Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision. But despite their popularity, the "correctness" of the implicitly-learned attention maps has only been assessed
Externí odkaz:
http://arxiv.org/abs/1605.09553
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, ac
Externí odkaz:
http://arxiv.org/abs/1604.04573
We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described. We
Externí odkaz:
http://arxiv.org/abs/1511.02283
In this paper, we present the mQA model, which is able to answer questions about the content of an image. The answer can be a sentence, a phrase or a single word. Our model contains four components: a Long Short-Term Memory (LSTM) to extract the ques
Externí odkaz:
http://arxiv.org/abs/1505.05612