Dense2Ad: Densely-Supervised Human-Object Interaction with Deep Convolutional Neural Networks


Dense2Ad: Densely-Supervised Human-Object Interaction with Deep Convolutional Neural Networks – In this paper, we propose a new framework for fully convolutional and unsupervised multi-modal vision that simultaneously leverages the information from the input image as well as the contextual information for learning the joint features. Firstly, a deep learning framework is proposed to achieve this in two steps: i) to extract the spatial relationships of the input images, i) to simultaneously learn a common feature from the input image, and ii) to jointly learn the features from both of the two images. Then, a supervised multi-modal image generation method is implemented to extract the contextual information. Experimental results from experiments show that our method outperforms the existing joint feature-wise CNN methods and achieves significant improvements in performance compared to the state-of-the-art multi-modal approaches.

In previous work, we used a dual asymmetric backpropagation scheme to optimize the stochastic gradient of the objective function. While we show empirically that the algorithm’s optimisation algorithm can be easily recovered from the non-zero bound, the dual asymmetric backpropagation algorithm was able to achieve a very fast convergence. Here, we demonstrate that the dual asymmetric backpropagation algorithm can be replaced by a non-zero bound for the optimal stochastic gradient.

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Dense2Ad: Densely-Supervised Human-Object Interaction with Deep Convolutional Neural Networks

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  • Learning Nonlinear Process Models for Deep Neural Networks

    Improved Active Learning Algorithm via Dual Asymmetric BackpropagationIn previous work, we used a dual asymmetric backpropagation scheme to optimize the stochastic gradient of the objective function. While we show empirically that the algorithm’s optimisation algorithm can be easily recovered from the non-zero bound, the dual asymmetric backpropagation algorithm was able to achieve a very fast convergence. Here, we demonstrate that the dual asymmetric backpropagation algorithm can be replaced by a non-zero bound for the optimal stochastic gradient.


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