7,886 research outputs found
Gibbs Max-margin Topic Models with Data Augmentation
Max-margin learning is a powerful approach to building classifiers and
structured output predictors. Recent work on max-margin supervised topic models
has successfully integrated it with Bayesian topic models to discover
discriminative latent semantic structures and make accurate predictions for
unseen testing data. However, the resulting learning problems are usually hard
to solve because of the non-smoothness of the margin loss. Existing approaches
to building max-margin supervised topic models rely on an iterative procedure
to solve multiple latent SVM subproblems with additional mean-field assumptions
on the desired posterior distributions. This paper presents an alternative
approach by defining a new max-margin loss. Namely, we present Gibbs max-margin
supervised topic models, a latent variable Gibbs classifier to discover hidden
topic representations for various tasks, including classification, regression
and multi-task learning. Gibbs max-margin supervised topic models minimize an
expected margin loss, which is an upper bound of the existing margin loss
derived from an expected prediction rule. By introducing augmented variables
and integrating out the Dirichlet variables analytically by conjugacy, we
develop simple Gibbs sampling algorithms with no restricting assumptions and no
need to solve SVM subproblems. Furthermore, each step of the
"augment-and-collapse" Gibbs sampling algorithms has an analytical conditional
distribution, from which samples can be easily drawn. Experimental results
demonstrate significant improvements on time efficiency. The classification
performance is also significantly improved over competitors on binary,
multi-class and multi-label classification tasks.Comment: 35 page
Fast and Accurate Neural Word Segmentation for Chinese
Neural models with minimal feature engineering have achieved competitive
performance against traditional methods for the task of Chinese word
segmentation. However, both training and working procedures of the current
neural models are computationally inefficient. This paper presents a greedy
neural word segmenter with balanced word and character embedding inputs to
alleviate the existing drawbacks. Our segmenter is truly end-to-end, capable of
performing segmentation much faster and even more accurate than
state-of-the-art neural models on Chinese benchmark datasets.Comment: To appear in ACL201
A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
Automatic License Plate Recognition (ALPR) has been a frequent topic of
research due to many practical applications. However, many of the current
solutions are still not robust in real-world situations, commonly depending on
many constraints. This paper presents a robust and efficient ALPR system based
on the state-of-the-art YOLO object detector. The Convolutional Neural Networks
(CNNs) are trained and fine-tuned for each ALPR stage so that they are robust
under different conditions (e.g., variations in camera, lighting, and
background). Specially for character segmentation and recognition, we design a
two-stage approach employing simple data augmentation tricks such as inverted
License Plates (LPs) and flipped characters. The resulting ALPR approach
achieved impressive results in two datasets. First, in the SSIG dataset,
composed of 2,000 frames from 101 vehicle videos, our system achieved a
recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better
than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%,
respectively) and considerably outperforming previous results (81.80%). Second,
targeting a more realistic scenario, we introduce a larger public dataset,
called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos
and 4,500 frames captured when both camera and vehicles are moving and also
contains different types of vehicles (cars, motorcycles, buses and trucks). In
our proposed dataset, the trial versions of commercial systems achieved
recognition rates below 70%. On the other hand, our system performed better,
with recognition rate of 78.33% and 35 FPS.Comment: Accepted for presentation at the International Joint Conference on
Neural Networks (IJCNN) 201
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