Multi-label Visual Place Matching


Multi-label Visual Place Matching – A major challenge in the area of Convolutional Neural Networks (CNN) is the lack of explicit representation of multiple target regions. In this work, we present a novel method which enables the learning of multiple target regions without supervision (i.e., labeling) at each instant. The method is based on a novel combination of an external feature learning algorithm and a novel network architecture, which is based on a local and long-term memory network. Experimental evaluation on two different datasets, MNIST and MS-DB, reveals that our method outperforms the state-of-the-art CNN architectures on the MNIST dataset to the same extent as or better than the state of the art models.

In this paper, we present a new technique for extracting 3D 3D shape from the 3D scene from a single image. We use a convolutional neural network to learn a sequence-to-sequence model for the 3D scene and train the convolutional neural network with such loss functions as 2D and 3D convolutional activations (3D+3D) as inputs. The proposed method allows us to model a 3D scene with complex 3D shape parameters and learn a sequence-to-sequence model in order to accurately predict the 3D shape from the input images. The sequence-to-sequence model is trained using the convolutional neural network in a learning and prediction network. In addition, two complementary loss functions of 2D and 3D feature (DME, DME-DME and DME+DME) as input are also used as discriminative loss functions to predict the 3D shape from the input images. The proposed model is the first to achieve promising performance on the challenging COCO dataset.

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Multi-label Visual Place Matching

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  • Machine Learning for the Classification of Pedestrian Data

    A New Biometric Approach for Retinal Vessel SegmentationIn this paper, we present a new technique for extracting 3D 3D shape from the 3D scene from a single image. We use a convolutional neural network to learn a sequence-to-sequence model for the 3D scene and train the convolutional neural network with such loss functions as 2D and 3D convolutional activations (3D+3D) as inputs. The proposed method allows us to model a 3D scene with complex 3D shape parameters and learn a sequence-to-sequence model in order to accurately predict the 3D shape from the input images. The sequence-to-sequence model is trained using the convolutional neural network in a learning and prediction network. In addition, two complementary loss functions of 2D and 3D feature (DME, DME-DME and DME+DME) as input are also used as discriminative loss functions to predict the 3D shape from the input images. The proposed model is the first to achieve promising performance on the challenging COCO dataset.


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