Computer vision publications
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Learn, Generate, Rank, Explain: A Case Study of Explanation by Generation
We propose a case study of a novel machine learning approach for generative searching and ranking of motion capture activities with visual explanation.
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Generalized Ternary Connect: End-to-End Learning and Compression of Multiplication-Free Deep Neural Networks
The use of deep neural networks in edge computing devices hinges on the balance between accuracy and complexity of computations. Ternary Connect (TC) \cite{lin2015neural} addresses this issue by restricting the…
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Human Motion Modeling using DVGANs
We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs).
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Zero-Shot Object Detection
We introduce and tackle the problem of zero-shot object detection, which aims to detect object classes which are not observed during training.
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Power-grid controller anomaly detection with enhanced temporal deep learning
Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can…
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Evaluating Visual-Semantic Explanations using a Collaborative Image Guessing Game
Abstract While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks.…
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Augmented Reality Driving Using Semantic Geo-Registration
We propose a new approach that utilizes semantic information to register 2D monocular video frames to the world using 3D georeferenced data, for augmented reality driving applications.
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Efficient Fine-Grained Classification and Part Localization Using One Compact Network
We propose a novel multi-task deep network architecture that jointly optimizes both localization of parts and fine-grained class labels by learning from training data.
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Utilizing Semantic Visual Landmarks for Precise Vehicle Navigation
This paper presents a new approach for integrating semantic information for vision-based vehicle navigation.
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Automated Image Analysis and Classification Tool Based on Computer Vision Deep Learning Technologies
We present a rapid underwater video and automated image analysis tool using computer vision deep learning technologies.
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Advances in Automated Stock Assessment Based on Computer Vision Deep Learning Technologies
We present a rapid fish assessment method leveraging computer vision deep learning technologies to provide both (1) rapid fish annotation and (2) fish classification with fish counting.
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BitNet: Bit-Regularized Deep Neural Networks
We present a novel optimization strategy for training neural networks which we call "BitNet". Our key idea is to limit the expressive power of the network by dynamically controlling the…