no code implementations • 1 Jun 2023 • Jiachen Li, Xinwei Shi, Feiyu Chen, Jonathan Stroud, Zhishuai Zhang, Tian Lan, Junhua Mao, Jeonhyung Kang, Khaled S. Refaat, Weilong Yang, Eugene Ie, CongCong Li
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.
no code implementations • 22 Dec 2021 • Jingxiao Zheng, Xinwei Shi, Alexander Gorban, Junhua Mao, Yang song, Charles R. Qi, Ting Liu, Visesh Chari, Andre Cornman, Yin Zhou, CongCong Li, Dragomir Anguelov
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 LiDAR, and a high bar for estimation accuracy.
no code implementations • CVPR 2020 • Zhishuai Zhang, Jiyang Gao, Junhua Mao, Yukai Liu, Dragomir Anguelov, Cong-Cong Li
For the Waymo Open Dataset, we achieve a bird-eyes-view (BEV) detection AP of 80. 73 and trajectory prediction average displacement error (ADE) of 33. 67cm for pedestrians, which establish the state-of-the-art for both tasks.
no code implementations • NeurIPS 2016 • Junhua Mao, Jiajing Xu, Yushi Jing, Alan Yuille
In this paper, we focus on training and evaluating effective word embeddings with both text and visual information.
no code implementations • 31 May 2016 • Chenxi Liu, Junhua Mao, Fei Sha, Alan Yuille
Attention mechanisms have recently been introduced in deep learning for various tasks in natural language processing and computer vision.
1 code implementation • CVPR 2016 • Jiang Wang, Yi Yang, Junhua Mao, Zhiheng Huang, Chang Huang, Wei Xu
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, actions and attributes in an image.
no code implementations • NeurIPS 2015 • Haoyuan Gao, Junhua Mao, Jie zhou, Zhiheng Huang, Lei Wang, Wei Xu
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
1 code implementation • CVPR 2016 • Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan Yuille, Kevin Murphy
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.
1 code implementation • NeurIPS 2015 • Haoyuan Gao, Junhua Mao, Jie zhou, Zhiheng Huang, Lei Wang, Wei Xu
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
1 code implementation • ICCV 2015 • Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan Yuille
In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task.
2 code implementations • 20 Dec 2014 • Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan Yuille
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions.
no code implementations • NeurIPS 2014 • Jun Zhu, Junhua Mao, Alan L. Yuille
We propose a novel learning algorithm called \emph{expectation loss SVM} (e-SVM) that is devoted to the problems where only the ``positiveness" instead of a binary label of each training sample is available.
no code implementations • 4 Oct 2014 • Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Alan L. Yuille
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel sentence descriptions to explain the content of images.