RoboJam: A Large Scale Framework for Multi-Label Image Monolingual Naming


RoboJam: A Large Scale Framework for Multi-Label Image Monolingual Naming – RoboJam is a platform for collaborative learning of robotic image objects over a small geographical area. It is also a platform to experiment with the use of a variety of natural images. Here, we present a new collaborative framework for the exploration of deep learning based on the robot vision system in the presence of noisy object environments.

Recently, deep neural networks have achieved remarkable successes in solving complex semantic action recognition tasks. However, the network’s effectiveness has been limited by low training volumes since the network is highly sensitive to small amounts of action data. In this paper, we propose a network architecture in which neurons are fed with a convolutional layer to encode action sequences. This layer is adapted by the network to encode deep convolutional representations of the input data, allowing for fast and accurate learning tasks. The convolution layer is composed of several layers, which encode long-term actions across frames, as well as sequences of different length depending on the input. The learning difficulty has been alleviated by a novel temporal information restoration method which employs a multi-scale temporal network to improve the performance by the network’s own decoding accuracy. Our network architecture is fully automatic and based on the idea of convolving the model into a temporal network, to better understand the underlying action sequence and to understand interactions between neurons. Experimental results on UCI and COCO datasets show the significant improvement achieved by the proposed network architecture.

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RoboJam: A Large Scale Framework for Multi-Label Image Monolingual Naming

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    Reconstructing Motion and Spatio-Templates in Close-to-Real World ScenesRecently, deep neural networks have achieved remarkable successes in solving complex semantic action recognition tasks. However, the network’s effectiveness has been limited by low training volumes since the network is highly sensitive to small amounts of action data. In this paper, we propose a network architecture in which neurons are fed with a convolutional layer to encode action sequences. This layer is adapted by the network to encode deep convolutional representations of the input data, allowing for fast and accurate learning tasks. The convolution layer is composed of several layers, which encode long-term actions across frames, as well as sequences of different length depending on the input. The learning difficulty has been alleviated by a novel temporal information restoration method which employs a multi-scale temporal network to improve the performance by the network’s own decoding accuracy. Our network architecture is fully automatic and based on the idea of convolving the model into a temporal network, to better understand the underlying action sequence and to understand interactions between neurons. Experimental results on UCI and COCO datasets show the significant improvement achieved by the proposed network architecture.


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