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RSA: Randomized Simulation as Augmentation for Robust Human Action Recognition
[article]
2019
arXiv
pre-print
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We generate large-scale synthetic datasets with randomized nuisance factors. ...
This is in large part due to the explosion of possible variation in video -- including lighting changes, object variation, movement variation, and changes in surrounding context. ...
The recent advances of action recognition are partially due to the creation of large-scale labeled video datasets [15, 5, 33] . ...
arXiv:1912.01180v1
fatcat:4g5bxwil4vcpjhxn3sc4sanwcy
Video-based descriptors for object recognition
2011
Image and Vision Computing
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Modules of our system relate to multi-scale feature selection, tracking, local descriptors, and bag-of-features classification, specifically on baseline algorithms [11] [12] [13] [14] . ...
This is currently under-played in favor of hand-labeled training data, but time can effectively act as a "weak supervisor" in visual recognition, and we attempt to tap on that role. ...
Acknowledgments This project was supported in part by ARO 56765-CI, ONR N00014-08-1-0414, AFOSR FA9550-09-1-0427. A video demonstration of the system can be seen at http://www.youtube.com/watch? ...
doi:10.1016/j.imavis.2011.08.003
fatcat:7mex6a6j3ff2pfc3o6w76auehu
Multi-scale features for identifying individuals in large biological databases: an application of pattern recognition technology to the marbled salamander Ambystoma opacum
2007
Journal of Applied Ecology
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We develop a pattern recognition algorithm and photo-identification method that uses photographs taken in the field to identify individual marbled salamanders ( Ambystoma opacum ), using their dorsal patterns ...
We develop, test, and apply a pattern recognition algorithm that enables efficient identification of individual marbled salamanders in a database exceeding 1000 images. ...
Acknowledgements The first two authors contributed equally to this work. We would like to thank B. Compton, A. Richmond, S. Jackson, C. Griffin, S. Melvin, C. Jenkins and B. ...
doi:10.1111/j.1365-2664.2007.01368.x
fatcat:r4aw2l36cbgirm7dnqr5wc7mty
Is Rotation a Nuisance in Shape Recognition?
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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Rotation in closed contour recognition is a puzzling nuisance in most algorithms. ...
and 3) how to use rotation unaware local features for rotation aware shape recognition? ...
Acknowledgement: we would like to thank Prof. David Jacobs and Arijit Biswas from the University of Maryland at College Park for generously providing the Leafsnap dataset. ...
doi:10.1109/cvpr.2014.528
dblp:conf/cvpr/KeL14
fatcat:bvvclc4zd5c6pd4jf3nu3rw5ri
A partial least squares framework for speaker recognition
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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In this space, a number of approaches to model inter-class separability and nuisance attribute variability have been proposed. ...
Modern approaches to speaker recognition (verification) operate in a space of "supervectors" created via concatenation of the mean vectors of a Gaussian mixture model (GMM) adapted from a universal background ...
Accelerating PLS: Despite the success of PLS, its O(N d) computational cost does not scale well for large sample sizes and large number of features. ...
doi:10.1109/icassp.2011.5947548
dblp:conf/icassp/SrinivasanZD11
fatcat:dodfty7wjfhv7b4kspkpwkwl4e
Classification Modulo Invariance, With Application to Face Recognition
2003
Journal of Computational And Graphical Statistics
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On the face recognition task, a classifier based on our techniques has an error rate that is 20% lower than that of the best algorithm in a reference software distribution. ...
them to a face recognition task. ...
Algorithm In describing the algorithms, when we refer to our example, we mean the face recognition work described in Section 4. A.1. ...
doi:10.1198/1061860032634
fatcat:z62ta5penze4pj77yswzusakfi
Permutation invariant SVMs
2006
Proceedings of the 23rd international conference on Machine learning - ICML '06
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Experiments are shown on character recognition, 3D object recognition and various UCI datasets. ...
This approach induces permutational invariance in the classifier which can then be directly applied to unusual set-based representations of data. ...
What is lacking in literature, to the best of our knowledge, are large margin discriminative algorithms that factor out nuisance permutations to maximize classification accuracy. ...
doi:10.1145/1143844.1143947
dblp:conf/icml/ShivaswamyJ06
fatcat:o5v7ewtiojhnljp4pb5juh54sm
Learning and matching multiscale template descriptors for real-time detection, localization and tracking
2011
CVPR 2011
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Each local descriptor aggregates contrast invariant statistics (normalized intensity and gradient orientation) across scales, in a way that enables matching under significant scale variations. ...
We describe a system to learn an object template from a video stream, and localize and track the corresponding object in live video. ...
Since the users' goal is to "explore" the object for later recognition, such a purpose is usually reflected in the video containing a fair sample of the nuisance distribution, as we will see in the discussion ...
doi:10.1109/cvpr.2011.5995453
dblp:conf/cvpr/LeeS11
fatcat:a7xpn22ojbebhma6tdnwuh7qzy
A Probabilistic Theory of Deep Learning
[article]
2015
arXiv
pre-print
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For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while speech recognition involves the unknown voice pronunciation, pitch, and ...
A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. ...
A special thanks to Xaq Pitkow whose keen insight, criticisms and detailed feedback on this work have been instrumental in its development. ...
arXiv:1504.00641v1
fatcat:dsqdeopvrjdhrhrd6ytmsjhyzq
TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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This operator is able to efficiently handle prior knowledge on nuisance variations in the data, such as rotation or scale changes. ...
On the other hand, we formulate features in convolutional neural networks to be transformation-invariant. ...
This is most probably due to the fact that fewer canonical positions needs to be handled by the learning algorithm. ...
doi:10.1109/cvpr.2016.38
dblp:conf/cvpr/LaptevSBP16
fatcat:56yxphtijjbrbofjkeg7wneaea
Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation
2012
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. ...
Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. ...
JW thanks Allen Yang of UC Berkeley EECS and Robert Fossum of UIUC Mathematics for discussions related to this work, and acknowledges support from a Microsoft Fellowship and the Lemelson-Illinois Student ...
doi:10.1109/tpami.2011.112
pmid:21646680
fatcat:z4dbgo5axrcepewttalavm3hse
Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach
[article]
2020
arXiv
pre-print
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Those nuisances constitute a large number of fine-grained domains, across which the detection model has to stay robust. ...
(NDFT), for the specific challenging problem of object detection in UAV images, achieving a substantial gain in robustness to those nuisances. ...
In comparison, the abundance of UAV-specific nuisances will cause the resulting UAV-based detection model to operate in a large number of different fine-grained domains. ...
arXiv:1908.03856v2
fatcat:d7gx37iz2nbrhiqg27axtl64nu
Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner
[article]
2021
arXiv
pre-print
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Consequently, on a large-scale UAVDT benchmark, it is shown that our framework can reduce the training time of NDFT from 31 hours to 3 hours while still maintaining the performance. ...
Previously, nuisance disentangled feature transformation (NDFT) was proposed to build domain-invariant feature extractor with with knowledge of nuisance factors. ...
We conducted vehicle detection on the UAVDT dataset, a large-scale benchmark, and showed that it performs better than baseline, not using nuisance factor predictions, and performs comparably with NDFT. ...
arXiv:2105.14693v1
fatcat:fwuztsfb25hrbg2archp2fmmzy
FACE RECOGNITION FROM VIDEO: A REVIEW
2012
International journal of pattern recognition and artificial intelligence
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The ensuing results have demonstrated that videos possess unique properties that allow both humans and automated systems to perform recognition accurately in difficult viewing conditions. ...
We also draw connections between the ways in which humans and current algorithms recognize faces. ...
The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of our sponsors. ...
doi:10.1142/s0218001412660024
fatcat:xztw7hmpsjacbogyn22axiq4tq
Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer
2013
2013 IEEE Conference on Computer Vision and Pattern Recognition
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Single-sample face recognition is one of the most challenging problems in face recognition. ...
The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. ...
We observe that in addition to the wellunderstood image nuisances aforementioned, one of the remaining challenges in face recognition is indeed the small sample set problem. ...
doi:10.1109/cvpr.2013.455
dblp:conf/cvpr/ZhuangYZSM13
fatcat:mirhr2iyvbfojji3pt3zd6b7ii
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