11,401 research outputs found
Adapting noise filters for ranking
Noise filtering can be considered an important preprocessing step in the data mining process, making data more reliable for pattern extraction. An interesting aspect for increasing data understanding would be to rank the potential noisy cases, in order to evidence the most unreliable instances to be further examined. Since the majority of the filters from the literature were designed only for hard classification, distinguishing whether an example is noisy or not, in this paper we adapt the output of some state of the art noise filters for ranking the cases identified as suspicious. We also present new evaluation measures for the noise rankers designed, which take into account the ordering of the detected noisy cases.FAPESPCNP
Discriminative Scale Space Tracking
Accurate scale estimation of a target is a challenging research problem in
visual object tracking. Most state-of-the-art methods employ an exhaustive
scale search to estimate the target size. The exhaustive search strategy is
computationally expensive and struggles when encountered with large scale
variations. This paper investigates the problem of accurate and robust scale
estimation in a tracking-by-detection framework. We propose a novel scale
adaptive tracking approach by learning separate discriminative correlation
filters for translation and scale estimation. The explicit scale filter is
learned online using the target appearance sampled at a set of different
scales. Contrary to standard approaches, our method directly learns the
appearance change induced by variations in the target scale. Additionally, we
investigate strategies to reduce the computational cost of our approach.
Extensive experiments are performed on the OTB and the VOT2014 datasets.
Compared to the standard exhaustive scale search, our approach achieves a gain
of 2.5% in average overlap precision on the OTB dataset. Additionally, our
method is computationally efficient, operating at a 50% higher frame rate
compared to the exhaustive scale search. Our method obtains the top rank in
performance by outperforming 19 state-of-the-art trackers on OTB and 37
state-of-the-art trackers on VOT2014.Comment: To appear in TPAMI. This is the journal extension of the
VOT2014-winning DSST tracking metho
Unsupervised Learning of Visual Representations using Videos
Is strong supervision necessary for learning a good visual representation? Do
we really need millions of semantically-labeled images to train a Convolutional
Neural Network (CNN)? In this paper, we present a simple yet surprisingly
powerful approach for unsupervised learning of CNN. Specifically, we use
hundreds of thousands of unlabeled videos from the web to learn visual
representations. Our key idea is that visual tracking provides the supervision.
That is, two patches connected by a track should have similar visual
representation in deep feature space since they probably belong to the same
object or object part. We design a Siamese-triplet network with a ranking loss
function to train this CNN representation. Without using a single image from
ImageNet, just using 100K unlabeled videos and the VOC 2012 dataset, we train
an ensemble of unsupervised networks that achieves 52% mAP (no bounding box
regression). This performance comes tantalizingly close to its
ImageNet-supervised counterpart, an ensemble which achieves a mAP of 54.4%. We
also show that our unsupervised network can perform competitively in other
tasks such as surface-normal estimation
Two-Dimensional Convolutional Recurrent Neural Networks for Speech Activity Detection
Speech Activity Detection (SAD) plays an important role in mobile communications and automatic speech recognition (ASR). Developing efficient SAD systems for real-world applications is a challenging task due to the presence of noise. We propose a new approach to SAD where we treat it as a two-dimensional multilabel image classification problem. To classify the audio segments, we compute their Short-time Fourier Transform spectrograms and classify them with a Convolutional Recurrent Neural Network (CRNN), traditionally used in image recognition. Our CRNN uses a sigmoid activation function, max-pooling in the frequency domain, and a convolutional operation as a moving average filter to remove misclassified spikes. On the development set of Task 1 of the 2019 Fearless Steps Challenge, our system achieved a decision cost function (DCF) of 2.89%, a 66.4% improvement over the baseline. Moreover, it achieved a DCF score of 3.318% on the evaluation dataset of the challenge, ranking first among all submissions
Improving the Sensitivity of Advanced LIGO Using Noise Subtraction
This paper presents an adaptable, parallelizable method for subtracting
linearly coupled noise from Advanced LIGO data. We explain the features
developed to ensure that the process is robust enough to handle the variability
present in Advanced LIGO data. In this work, we target subtraction of noise due
to beam jitter, detector calibration lines, and mains power lines. We
demonstrate noise subtraction over the entirety of the second observing run,
resulting in increases in sensitivity comparable to those reported in previous
targeted efforts. Over the course of the second observing run, we see a 30%
increase in Advanced LIGO sensitivity to gravitational waves from a broad range
of compact binary systems. We expect the use of this method to result in a
higher rate of detected gravitational-wave signals in Advanced LIGO data.Comment: 15 pages, 6 figure
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