Towards Large-grained Visual Classification by Optimizing for Hierarchical Feature Learning


Towards Large-grained Visual Classification by Optimizing for Hierarchical Feature Learning – The main issue of the current paper is the problem of finding an efficient algorithm for estimating an arbitrary tree class from a graph. We propose a new method for estimating a tree class based on the non-deterministic non-distribution between leaf nodes and a graph. We show that our algorithm can produce tree class accuracies comparable to or better than a state-of-the-art linear regression algorithm. Furthermore, we show that a simple algorithm with the same error rate is the best choice of the algorithm.

Although the human visual system has proven to perform very well on a broad range of tasks, it remains a challenging task and difficult domain for machine vision (MVD). In this paper we propose a new deep-learning approach to MVD for video. The proposed approach consists in utilizing a recurrent neural network (RNN), which automatically learns a sequence representation based on the visual information (saccades), and a deep-learning neural network (DNN). The discriminative and discriminative networks are trained to predict video sequences. A novel training scheme, which is jointly optimized with the deep networks, is presented. Experiments on the recently proposed dataset BUG3 show that our approach outperforms previous methods by a considerable margin when compared to the state-of-the-art MVD approaches.

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Towards Large-grained Visual Classification by Optimizing for Hierarchical Feature Learning

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  • Neural-based Word Sense Disambiguation with Knowledge-base Fusion

    Deep Learning for Real-time Vehicle Detection through Deep Recurrent Neural NetworksAlthough the human visual system has proven to perform very well on a broad range of tasks, it remains a challenging task and difficult domain for machine vision (MVD). In this paper we propose a new deep-learning approach to MVD for video. The proposed approach consists in utilizing a recurrent neural network (RNN), which automatically learns a sequence representation based on the visual information (saccades), and a deep-learning neural network (DNN). The discriminative and discriminative networks are trained to predict video sequences. A novel training scheme, which is jointly optimized with the deep networks, is presented. Experiments on the recently proposed dataset BUG3 show that our approach outperforms previous methods by a considerable margin when compared to the state-of-the-art MVD approaches.


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