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Towards Accurate Multi-person Pose Estimation in the Wild

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Papandreou, George, Zhu, Tyler, Kanazawa, Nori, Toshev, Alexander, Tompson, Jonathan, Bregler, Chris, Murphy, Kevin We propose a method for multi-person detection and 2-D pose estimation that achieves state-of-art results on...

In-network Neural Networks

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Siracusano, Giuseppe, Bifulco, Roberto We present N2Net, a system that implements binary neural networks using commodity switching chips deployed in network switches and routers. Our system shows that these devices...

Efficient Object Localization Using Convolutional Networks

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Tompson, Jonathan, Goroshin, Ross, Jain, Arjun, LeCun, Yann, Bregler, Christopher Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling...

Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

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Alexander Wong, Mohammad Javad Shafiee, Francis Li, Brendan Chwyl Object detection is a major challenge in computer vision, involving both object classification and object localization within a scene. While deep...

Deep Learning with Limited Numerical Precision

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Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, Pritish Narayanan Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data...

Object detection at 200 Frames Per Second

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Mehta, Rakesh, Ozturk, Cemalettin In this paper, we propose an efficient and fast object detector which can process hundreds of frames per second. To achieve this goal we investigate three...

Object Detection in Video with Spatiotemporal Sampling Networks

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Bertasius, Gedas, Torresani, Lorenzo, Shi, Jianbo We propose a Spatiotemporal Sampling Network (STSN) that uses deformable convolutions across time for object detection in videos. Our STSN performs object detection in...

Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate...

Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Zhifeng Chen GPipe is a scalable pipeline parallelism library that enables learning of giant deep neural networks....

Adrien Laversanne-Finot, Alexandre Péré, Pierre-Yves Oudeyer Intrinsically motivated goal exploration processes enable agents to autonomously sample goals to explore efficiently complex environments with high-dimensional continuous actions. They have been applied...