794 research outputs found
Efficient Data Analytics on Augmented Similarity Triplets
Many machine learning methods (classification, clustering, etc.) start with a
known kernel that provides similarity or distance measure between two objects.
Recent work has extended this to situations where the information about objects
is limited to comparisons of distances between three objects (triplets). Humans
find the comparison task much easier than the estimation of absolute
similarities, so this kind of data can be easily obtained using crowd-sourcing.
In this work, we give an efficient method of augmenting the triplets data, by
utilizing additional implicit information inferred from the existing data.
Triplets augmentation improves the quality of kernel-based and kernel-free data
analytics tasks. Secondly, we also propose a novel set of algorithms for common
supervised and unsupervised machine learning tasks based on triplets. These
methods work directly with triplets, avoiding kernel evaluations. Experimental
evaluation on real and synthetic datasets shows that our methods are more
accurate than the current best-known techniques
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Recently, crowdsourcing has emerged as an effective paradigm for
human-powered large scale problem solving in various domains. However, task
requester usually has a limited amount of budget, thus it is desirable to have
a policy to wisely allocate the budget to achieve better quality. In this
paper, we study the principle of information maximization for active sampling
strategies in the framework of HodgeRank, an approach based on Hodge
Decomposition of pairwise ranking data with multiple workers. The principle
exhibits two scenarios of active sampling: Fisher information maximization that
leads to unsupervised sampling based on a sequential maximization of graph
algebraic connectivity without considering labels; and Bayesian information
maximization that selects samples with the largest information gain from prior
to posterior, which gives a supervised sampling involving the labels collected.
Experiments show that the proposed methods boost the sampling efficiency as
compared to traditional sampling schemes and are thus valuable to practical
crowdsourcing experiments.Comment: Accepted by AAAI201
Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review
With the large chunks of social media data being created daily and the
parallel rise of realistic multimedia tampering methods, detecting and
localising tampering in images and videos has become essential. This survey
focusses on approaches for tampering detection in multimedia data using deep
learning models. Specifically, it presents a detailed analysis of benchmark
datasets for malicious manipulation detection that are publicly available. It
also offers a comprehensive list of tampering clues and commonly used deep
learning architectures. Next, it discusses the current state-of-the-art
tampering detection methods, categorizing them into meaningful types such as
deepfake detection methods, splice tampering detection methods, copy-move
tampering detection methods, etc. and discussing their strengths and
weaknesses. Top results achieved on benchmark datasets, comparison of deep
learning approaches against traditional methods and critical insights from the
recent tampering detection methods are also discussed. Lastly, the research
gaps, future direction and conclusion are discussed to provide an in-depth
understanding of the tampering detection research arena
Person Re-identification with Deep Learning
In this work, we survey the state of the art of person re-identification and introduce the basics of the deep learning method for implementing this task. Moreover, we propose a new structure for this task.
The core content of our work is to optimize the model that is composed of a pre-trained network to distinguish images from different people with representative features. The experiment is implemented on three public person datasets and evaluated with evaluation metrics that are mean Average Precision (mAP) and Cumulative Matching Characteristic (CMC).
We take the BNNeck structure proposed by Luo et al. [25] as the baseline model. It adopts several tricks for the training, such as the mini-batch strategy of loading images, data augmentation for improving the model’s robustness, dynamic learning rate, label-smoothing regularization, and the L2 regularization to reach a remarkable performance. Inspired from that, we propose a novel structure named SplitReID that trains the model in separated feature embedding spaces with multiple losses, which outperforms the BNNeck structure and achieves competitive performance on three datasets. Additionally, the SplitReID structure holds the property of lightweight computation complexity that it requires fewer parameters for the training and inference compared to the BNNeck structure.
Person re-identification can be deployed without high-resolution images and fixed angle of pedestrians with the deep learning method to achieve outstanding performance. Therefore, it holds an immeasurable prospect in practical applications, especially for the security fields, even though there are still some challenges like occlusions to be overcome
Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization
Principal component analysis (PCA) is widely used for dimensionality
reduction, with well-documented merits in various applications involving
high-dimensional data, including computer vision, preference measurement, and
bioinformatics. In this context, the fresh look advocated here permeates
benefits from variable selection and compressive sampling, to robustify PCA
against outliers. A least-trimmed squares estimator of a low-rank bilinear
factor analysis model is shown closely related to that obtained from an
-(pseudo)norm-regularized criterion encouraging sparsity in a matrix
explicitly modeling the outliers. This connection suggests robust PCA schemes
based on convex relaxation, which lead naturally to a family of robust
estimators encompassing Huber's optimal M-class as a special case. Outliers are
identified by tuning a regularization parameter, which amounts to controlling
sparsity of the outlier matrix along the whole robustification path of (group)
least-absolute shrinkage and selection operator (Lasso) solutions. Beyond its
neat ties to robust statistics, the developed outlier-aware PCA framework is
versatile to accommodate novel and scalable algorithms to: i) track the
low-rank signal subspace robustly, as new data are acquired in real time; and
ii) determine principal components robustly in (possibly) infinite-dimensional
feature spaces. Synthetic and real data tests corroborate the effectiveness of
the proposed robust PCA schemes, when used to identify aberrant responses in
personality assessment surveys, as well as unveil communities in social
networks, and intruders from video surveillance data.Comment: 30 pages, submitted to IEEE Transactions on Signal Processin
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