Multi-Instance Dictionary Learning for Classification and Segmentation


Multi-Instance Dictionary Learning for Classification and Segmentation – We propose a simple, elegant, and efficient way of learning to compute a high-level representation of an object instance. We train and evaluate several existing methods for object instance computation in both high-level and low-level representations, such as object representation learning and object instance segmentation. Our approach, named as Part-of-Class Representation Deep Learning (P3DRL), uses an information-theoretic framework to learn low-level object instance representations using object instance descriptors in a deep neural network. P3DRL significantly outperforms a variety of state-of-the-art CNN-based approaches in achieving state-of-the-art performance in object instance annotation on both high-level and low-level labels. We show that the proposed algorithm can effectively handle a variety of object instances, providing the ability to learn the object instances and their attributes at a scale of one, and a bounding box.

We present a new method for solving linear classifiers on graphs using the model-independent conditional likelihood. Our method performs well on some datasets such as MNIST dataset and CIFAR-10 dataset. We show that our method yields a state-of-the-art performance for linear classifiers.

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Multi-Instance Dictionary Learning for Classification and Segmentation

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  • Multi-target tracking without line complementation

    Practical recommendations for optimal and iterative learningWe present a new method for solving linear classifiers on graphs using the model-independent conditional likelihood. Our method performs well on some datasets such as MNIST dataset and CIFAR-10 dataset. We show that our method yields a state-of-the-art performance for linear classifiers.


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