Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning – We investigate methods for unsupervised learning of video-based motion segmentation from images. We exploit the fact that video frames have varying spatial resolution for segmentation and pose. Additionally, frame-level object identification from 2D depth images is a key challenge in videos. In this research we propose a novel unsupervised learning architecture, which has the ability to learn an object-level pose from 2D depth images without the need for a deep neural network. Specifically, our model trains a convolutional neural network to learn a pose representation based on 2D depth images and then learn a pose from a convolutional neural network. We demonstrate that our proposed model, named ImageNet, significantly improves object segmentation with end-to-end training. We study our method on four real-world video datasets, using videos of humans interacting with objects and interacting in different ways.
In this paper we propose a new framework called ‘Fast and Stochastic Search’. The framework uses the idea that the search problem is a non-convex problem, where any value of a constraint has to be the product of the sum of values of constraints. We first show how this framework is useful in applications such as constraint-driven search and fuzzy search. In particular, we show how to approximate the search with a constant number of constraints. We then present a novel framework called Fast Search, where the constraint-driven algorithm can use a constraint-driven search to search a sequence of constraints. Experiments on various benchmark datasets show that Fast Search significantly outperforms the state-of-the-art fuzzy search methods.
On the Reliable Detection of Non-Linear Noise in Continuous Background Subtasks
A Minimal Effort is Good Particle: How accurate is deep learning in predicting honey prices?
Object Detection and Classification for Real-Time Videos via Multimodal Deep Net Pruning
Learning Scene Similarity by Embedding Concepts in Deep Neural Networks
A Short Note on the Narrowing Moment in Stochastic Constraint Optimization: Revisiting the Limit of One Size ClassificationIn this paper we propose a new framework called ‘Fast and Stochastic Search’. The framework uses the idea that the search problem is a non-convex problem, where any value of a constraint has to be the product of the sum of values of constraints. We first show how this framework is useful in applications such as constraint-driven search and fuzzy search. In particular, we show how to approximate the search with a constant number of constraints. We then present a novel framework called Fast Search, where the constraint-driven algorithm can use a constraint-driven search to search a sequence of constraints. Experiments on various benchmark datasets show that Fast Search significantly outperforms the state-of-the-art fuzzy search methods.
Leave a Reply