Convolutional neural network with spatiotemporal-convex relaxations


Convolutional neural network with spatiotemporal-convex relaxations – We study the problem of optimizing a linear loss, and propose a new formulation with new sparsifying loss functions. Unlike previous sparsifying loss functions, the new sparsifying loss function only chooses the minimizer for the given loss, and uses a different optimization strategy to efficiently find the minimizer. We prove a new theoretical result, that a linear loss can be guaranteed to be optimal in the polynomial sense. Such optimization is computationally intractable, and is therefore restricted to the case in which training and inference are performed with a fixed distribution. Experiments on a practical benchmark dataset illustrate the properties of our loss.

This paper presents an approach to segment and classify human action recognition tasks. Motivated by human action and visual recognition we use an ensemble of three human action recognition tasks to classify action images and use an explicit representation of their input labels. Based on a new metric used to classify action images, we propose to use an ensemble of visual tracking models (e.g. the multi-view or multi-label approach) to classify the recognition tasks. Our visual tracking model aims at maximizing the information flow between visual and non-visual features, which allows for better segmentation and classification accuracy. We evaluate our approach using a dataset of over 30,000 labeled action images from various action recognition tasks and compare to state-of-the-art segmentation and classification performance, using an analysis of the visual recognition task. Our method consistently outperforms the state-of-the-art on both tasks.

Towards Information Compilation in Machine Learning

Towards a real-time CNN end-to-end translation

Convolutional neural network with spatiotemporal-convex relaxations

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  • A unified approach to multilevel modelling: Graph, Graph-Clique, and Clustering

    Learning from Imprecise Measurements by Transferring Knowledge to An Explicit ClassifierThis paper presents an approach to segment and classify human action recognition tasks. Motivated by human action and visual recognition we use an ensemble of three human action recognition tasks to classify action images and use an explicit representation of their input labels. Based on a new metric used to classify action images, we propose to use an ensemble of visual tracking models (e.g. the multi-view or multi-label approach) to classify the recognition tasks. Our visual tracking model aims at maximizing the information flow between visual and non-visual features, which allows for better segmentation and classification accuracy. We evaluate our approach using a dataset of over 30,000 labeled action images from various action recognition tasks and compare to state-of-the-art segmentation and classification performance, using an analysis of the visual recognition task. Our method consistently outperforms the state-of-the-art on both tasks.


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