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Found 22 papers, 9 papers with code
Towards a Real-Time Facial Analysis System
Facial analysis is an active research area in computer vision, with many practical applications.
On the Importance of Encrypting Deep Features
On the other hand, it is advisable to encrypt feature vectors, especially for a machine learning model in production.
using multiple losses for accurate facial age estimation
Age estimation is an essential challenge in computer vision.
Sample selection for efficient image annotation
Supervised object detection has been proven to be successful in many benchmark datasets achieving human-level performances.
Selective Probabilistic Classifier Based on Hypothesis Testing
In this paper, we propose a simple yet effective method to deal with the violation of the Closed-World Assumption for a classifier.
Handwritten Character Recognition from Wearable Passive RFID
The data is collected sequentially, such that we record both the stroke order and the resulting bitmap.
Adaptive L2 Regularization in Person Re-Identification
We introduce an adaptive L2 regularization mechanism in the setting of person re-identification.
Ranked #4 on
Person Re-Identification
on MSMT17
Iterative Bounding Box Annotation for Object Detection
Manual annotation of bounding boxes for object detection in digital images is tedious, and time and resource consuming.
Vehicle Attribute Recognition by Appearance: Computer Vision Methods for Vehicle Type, Make and Model Classification
This paper studies vehicle attribute recognition by appearance.
Bayesian Receiver Operating Characteristic Metric for Linear Classifiers
We derive a closed-form solution of the proposed accuracy metric for any linear binary classifier under the Gaussianity assumption, and study the accuracy of the proposed estimator using simulated and real-world data.
Surface Type Classification for Autonomous Robot Indoor Navigation
In this work we describe the preparation of a time series dataset of inertial measurements for determining the surface type under a wheeled robot.
HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learning
Recent advancements in machine learning research, i. e., deep learning, introduced methods that excel conventional algorithms as well as humans in several complex tasks, ranging from detection of objects in images and speech recognition to playing difficult strategic games.
Elastic Neural Networks for Classification
In this work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients.
Real Time System for Facial Analysis
In this paper we describe the anatomy of a real-time facial analysis system.
Acoustic Scene Classification: A Competition Review
In this paper we study the problem of acoustic scene classification, i. e., categorization of audio sequences into mutually exclusive classes based on their spectral content.
Binarized Convolutional Neural Networks for Efficient Inference on GPUs
This representation completely eliminates the need for floating point multiplications and additions and decreases both the computational load and the memory footprint compared to a full-precision network implemented in floating point, making it well-suited for resource-constrained environments.
Classification of Building Information Model (BIM) Structures with Deep Learning
In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other.
Faster Bounding Box Annotation for Object Detection in Indoor Scenes
The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations.
Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision
We propose a new framework for image classification with deep neural networks.
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure.
Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs).
Car Type Recognition with Deep Neural Networks
In this paper we study automatic recognition of cars of four types: Bus, Truck, Van and Small car.
