nils-herrmann/clean_scirepeval huggingface dataset:
Structured neural networks for pattern recognition

This paper proposes a novel approach for the design of structures of neural networks for pattern recognition. The basic idea lies in subdividing the whole classification problem in smaller and simpler problems at different levels, each managed by appropriate components of a complex neural architecture. Three neural structures are presented and applied in a surveillance system aimed at monitoring a railway waiting room classifying potential dangerous situations. Each architecture is composed by nodes, which are actual multilayer perceptrons trained to discriminate between subsets of classes until a complete separation among the classes is achieved. This approach showed better performances with respect to a classical statistical classification procedures and to a single neural network.
Automatic Tractography Analysis through Sparse Networks in Case-Control Studies

Magnetic Resonance Diffusion Tensor Imaging (DTI) has opened the way to a variety of white matter analysis approaches which leverage the axon networks in the brain. Even if tractography algorithms are widely used to reconstruct these networks, their topology is seldom employed to evaluate differences between patient and control groups, unless there is prior knowledge on the areas of interest. Using a sparse approach, we have developed a multivariate method to automatically identify the most significant connections bundles able to characterise differences between two groups. This will allow neuroscientist to explore inter-group differences in white-matter topology in an unbiased-fashion, and without the need of a priori knowledge. Here, we performed a preliminary test of the approach with serotonin dysfunctional mice and a control group. The results allowed us to identify inter-group differences in the density of white matter tracts originating from serotonergic areas, thus corroborating the predictive validity of the method.
Case-control discrimination through effective brain connectivity

Functional and structural connectivity convey different information about the brain. The integration of these different approaches is receiving growing attention from the research community, as it can shed new light on brain functions. This manuscript proposes a constrained autoregressive model with different lag-orders generating an “effective” connectivity matrix which models the structural connectivity integrating the functional activity. Multiple orders are investigated to observe how different time dependencies influence the effective connectivity. The proposed approach alters an initial structural connectivity representation according to functional data, by minimizing the reconstruction error of an autoregressive model constrained by the structural prior. The model is further validated in a case-control experiment, which aims at differentiating healthy subject and young patients affected by autism spectrum disorder.
Atlas-free connectivity analysis driven by white matter structure

Diffusion tensor imaging allows to infer brain connectivity from white matter, which can then be investigated aiming at finding possible biomarkers of disease. The usual initial step in graph construction is to identify the nodes in the brain using a predefined atlas. However, atlases are usually not considering the white matter structure. As a result, atlas-based brain parcellation and, hence, brain graphs are not fully considering the white matter organization. In this work, we are proposing an atlas-free scheme to map the structural brain networks. The idea is to identify the nodes in the brain exploiting the white matter structure inferred from the data. We first retrieve the white matter pathways from DTI, grouping fiber tracts into bundles. We then use these pathways in a clustering pipeline to identify the brain regions to map into the graph nodes, which are used to define the brain connectivity. We empirically tested the goodness of the proposed approach on a known case-control study obtaining results confirming findings in related literature.
Data-driven study of mouse sleep-stages using Restricted Boltzmann Machines

Rodents play an important role in sleep studies since they are the most easily available low-cost animal species sharing most genes and gene functions with humans. The scoring of sleep stages in these studies is usually based on a manual analysis of long-lasting recordings of the brain electrical activity and the activity of skeletal muscles. Hence, there is a great need for tools automating the investigation over large experiments. In this paper we present a way of analyzing this huge amount of electrophysiological data using unsupervised learning models. We show how employing latent variable models, like Restricted Boltzmann Machines, we can define different sleep-wakefulness sub-stages reflected by regularities in the data, without any prior knowledge. Our analysis shows that we can effectively discover meaningful feature representations which characterize sleep stages at a finer level than those commonly used by the experts. These feature representations can be also used to further characterize different mouse genotypes, without incurring biases of experts like in a classical analysis setup where predefined rules limit the discovery of novel insights.
Characterizing Humans on Riemannian Manifolds

In surveillance applications, head and body orientation of people is of primary importance for assessing many behavioral traits. Unfortunately, in this context people are often encoded by a few, noisy pixels so that their characterization is difficult. We face this issue, proposing a computational framework which is based on an expressive descriptor, the covariance of features. Covariances have been employed for pedestrian detection purposes, actually a binary classification problem on Riemannian manifolds. In this paper, we show how to extend to the multiclassification case, presenting a novel descriptor, named weighted array of covariances, especially suited for dealing with tiny image representations. The extension requires a novel differential geometry approach in which covariances are projected on a unique tangent space where standard machine learning techniques can be applied. In particular, we adopt the Campbell-Baker-Hausdorff expansion as a means to approximate on the tangent space the genuine (geodesic) distances on the manifold in a very efficient way. We test our methodology on multiple benchmark datasets, and also propose new testing sets, getting convincing results in all the cases.
Person re-identification using sparse representation with manifold constraints

Human re-identification is still a challenging task due to the human pose and illumination variations. Nowadays, surveillance cameras with high frame rate are capable of capturing several consecutive frames from each person. Multi-shot images provide richer information of the target person compared to a single-shot image. They, however, produce a high cost of information redundancy which may degrade the performance of re-identification systems. In this paper, we propose a novel framework that combines sparse coding and manifold constraints to extract discriminative information from multi-shot images of one pedestrian for person re-identification across a set of non-overlapped surveillance cameras. The evaluation over two standard multi-shot datasets shows very competitive accuracy of our framework against the state-of-the-art.
Weighted bag of visual words for object recognition

Bag of Visual words (BoV) is one of the most successful strategy for object recognition, used to represent an image as a vector of counts using a learned vocabulary. This strategy assumes that the representation is built using patches that are either densely extracted or sampled from the images using feature detectors. However, the dense strategy captures also the noisy background information, whereas the feature detection strategy can lose important parts of the objects. In this paper we propose a solution in-between these two strategies, by densely extracting patches from the image, and weighting them accordingly to their salience. Intuitively, highly salient patches have an important role in describing an object, while those with low saliency are still taken with low emphasis, instead of discarding them. We embed this idea in the word encoding mechanism adopted in the BoV approaches. The technique is successfully applied to vector quantization and Fisher vector, on Caltech-101 and Caltech-256.
An FPGA-based Classification Architecture on Riemannian Manifolds

In Computer Vision and Pattern Recognition, the object detection problem is a fundamental task, but only a few systems are thought to be realized on an embedded architecture. To this end, we propose an effective, low-latency, affordable classification architecture, especially suited for embedded platforms. In particular, we have designed a novel highly-parallelizable classification framework for an FPGA-based implementation, which is suitable for generic detection problems. The underlying model consists in a weighted sum of boosted binary classifiers, learned on a set of overlapped image patches. Each patch is described by estimating the covariance matrix of a set of features, so forming a very compact and expressive descriptor. Covariances matrices live on Riemannian Manifold, whose topology is particularly simple, so that they can be approximated in the Euclidean Vector Space in a cheap and conservative way. The hardware design has been developed in a parallel fashion and with specific architectural solutions, allowing a fast response without degrading the functional performances. We finally specialize this architecture to the challenging pedestrian detection problem, defining state-of-the art results on the standard INRIA pedestrian benchmark dataset.
Integrated region- and pixel-based approach to background modelling

In this paper a new probabilistic method for background modelling is proposed, aimed at the application in video surveillance tasks using a monitoring static camera. Recently, methods employing time-adaptive, per pixel, mixture of Gaussians (TAPPMOG) modelling have become popular due to their intrinsic appealing properties. Nevertheless, they are not able per se to monitor global changes in the scene, because they model the background as a set of independent pixel processes. In this paper, we propose to integrate this kind of pixel-based information with higher level region-based information, that permits one to manage also sudden changes of the background. These pixel- and region-based modules are naturally and effectively embedded in a probabilistic Bayesian framework called particle filtering, that allows a multi-object tracking. Experimental comparison with a classic pixel-based approach reveals that the proposed method is really effective in recovering from situations of sudden global illumination changes of the background, as well as limited non-uniform changes of the scene illumination.
Semi-supervised multi-feature learning for person re-identification

Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. On the other hand, learning-based methods are usually based on simpler features, and are trained on pairs of cameras to discriminate between individuals. In this paper, we present a method that joins these two ideas: given an arbitrary state-of-the-art set of features, no matter their number, dimensionality or descriptor, the proposed multi-class learning approach learns how to fuse them, ensuring that the features agree on the classification result. The approach consists of a semi-supervised multi-feature learning strategy, that requires at least a single image per person as training data. To validate our approach, we present results on different datasets, using several heterogeneous features, that set a new level of performance in the person re-identification problem.
Sparse representation classification with manifold constraints transfer

The fact that image data samples lie on a manifold has been successfully exploited in many learning and inference problems. In this paper we leverage the specific structure of data in order to improve recognition accuracies in general recognition tasks. In particular we propose a novel framework that allows to embed manifold priors into sparse representation-based classification (SRC) approaches. We also show that manifold constraints can be transferred from the data to the optimized variables if these are linearly correlated. Using this new insight, we define an efficient alternating direction method of multipliers (ADMM) that can consistently integrate the manifold constraints during the optimization process. This is based on the property that we can recast the problem as the projection over the manifold via a linear embedding method based on the Geodesic distance. The proposed approach is successfully applied on face, digit, action and objects recognition showing a consistently increase on performance when compared to the state of the art.
Efficient pooling of image based CNN features for action recognition in videos

In this paper, we propose a new video representation incorporating image based deep features and an efficient pooling strategy for the purpose of action recognition. The Convolutional Neural Network (CNN) based features have very recently emerged as the new state of the art for image classification. Several attempts have been made to extend such CNN models for videos by explicitly focusing on the temporal evolution of the frames. Feature pooling is one such approach which represents video sequences in terms of some statistical properties of the feature dimensions over the frames. However, traditional pooling strategies including max or average pooling explicitly fail to capture the temporal progression of the frame-level contents. In contrast to previous pooling techniques, we propose a two-level video representation which separately focuses on the entire video as well as a number of video sub-volumes. In both levels, we introduce a generic time series pooling on the frame-level deep CNN features efficiently. Further, a self-tuning spectral clustering is considered to highlight video snippets which are highly probable to contain significant sub-action sequences. We validate the proposed feature encoding on the challenging KTH-actions and UCF-50 datasets and find that the proposed encoding outperforms traditional pooling based feature representations by substantial margin.
Adaptive Local Movement Modeling for Robust Object Tracking

In this paper, we present a new strategy for modeling the motion of local patches for single-object tracking that can be seamlessly applied to most part-based trackers in the literature. The proposed adaptive local movement modeling method is able to model the local movement distribution of the image patches defining the object to track and the reliability of each image patch. Given the output of a base tracking algorithm, a Gaussian mixture model (GMM) is first used to model the distribution of the movement of local patches relative to the center of gravity of the tracked object. Then, the GMM is combined with the chosen base tracker in a boosting framework, which gives an efficient integrated scheme for the tracking task. This provides a robust procedure to detect outliers in the local motion of the patches. The algorithm is highly configurable with the possibility to change the number of local patches used for tracking and to adapt to the variations of the tracked object. The extensive tracking results on standard data sets show that equipping state-of-the-art trackers with our technique remarkably improves their performance.
Nonlinear Mappings for Generative Kernels on Latent Variable Models

Generative kernels have emerged in the last years as an effective method for mixing discriminative and generative approaches. In particular, in this paper, we focus on kernels defined on generative models with latent variables (e.g. the states in a Hidden Markov Model). The basic idea underlying these kernels is to compare objects, via a inner product, in a feature space where the dimensions are related to the latent variables of the model. Here we propose to enhance these kernels via a nonlinear normalization of the space, namely a nonlinear mapping of space dimensions able to exploit their discriminative characteristics. In this paper we investigate three possible nonlinear mappings, for two HMM-based generative kernels, testing them in different sequence classification problems, with really promising results.
Dissimilarity-based representation for local parts

In this paper a novel approach for dissimilarity-based representation is presented, which combines local image descriptors with several dissimilarity functions. The basic idea consists of defining the set of prototypes in terms of local descriptors of image parts, namely feature points extracted from the training set. Therefore, according to the dissimilarity-based approach, a new image can be characterized on the basis of its dissimilarity with each of the given prototypes. This leads to a new class of Local Kernels which exploits the use of dissimilarities between image parts. In particular, we show that the classic Bag-of-Feature (BoF) kernel can be revised as a special case of our new formulation, and better performance can be obtained when new dissimilarity functions are employed. Moreover, we observe that any variants of the basic BoF kernel can take advantage from our approach as we show for the case of the Pyramid Match kernel. Promising results are shown for image categorization on the ETH-80 database.
Exploiting multiple detections to learn robust brightness transfer functions in re-identification systems

Re-identification systems aim at recognizing the same individuals in multiple cameras and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of Cumulative Weighted Brightness Transfer Functions to model this appearance variations. It is multiple frame-based learning approach which leverages consecutive detections of each individual to transfer the appearance, rather than learning brightness transfer function from pairs of images. We tested our approach on standard multi-camera surveillance datasets showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based method.
Collaborative particle filters for group tracking

Tracking groups of people is a highly informative task in surveillance, and it represents a still open and little explored issue. In this paper, we propose a brand new framework for group tracking, that consists in two separate particle filters, one focusing on groups as atomic entities (the multi-group tracker), and the other modeling each individual separately (the multi-object tracker). The latter helps the multi-group tracker in better defining the nature of a group, evaluating the membership of each individual with respect to different groups, and allowing a robust management of the occlusions. The coupling of the two processes is theoretically founded due to the revision of the posterior distribution of the multi-group tracker with the statistics accumulated by the multi-object tracker. Experimental comparative results certify the goodness of the proposed technique.
Visual surveillance by depth from focus

This paper aims at describing a visual surveillance prototype system able to monitor a bounded (in depth) area by using a depth-from-focus approach. The single visual camera utilized is characterized by an adjustable lens objective controlled by a host computer. In this way, the system is capable to detect possible intruders entering in the controlled zone, recovering the range information by a depth-from-focus method, and tracking the incoming entity by using a simple strategy. Tracking is performed by computing the range distance such as to keep in focus the moving object that has violated the area. This method is based on an adaptive regulation strategy of the camera parameters which makes the system robust to lighting variations occurring in real environment. Preliminary results show the goodness of the proposed approach, especially concerning the depth information recovery.<<ETX>>
Multi-scale f-formation discovery for group detection

We present an unsupervised approach for the automatic detection of static interactive groups. The approach builds upon a novel multi-scale Hough voting policy, which incorporates in a flexible way the sociological notion of group as F-formation; the goal is to model at the same time small arrangements of close friends and aggregations of many individuals spread over a large area. Our technique is based on a competition of different voting sessions, each one specialized for a particular group cardinality; all the votes are then evaluated using information theoretic criteria, producing the final set of groups. The proposed technique has been applied on public benchmark sequences and a novel cocktail party dataset, evaluating new group detection metrics and obtaining state-of-the-art performances.
Joint Individual-Group Modeling for Tracking

We present a novel probabilistic framework that jointly models individuals and groups for tracking. Managing groups is challenging, primarily because of their nonlinear dynamics and complex layout which lead to repeated splitting and merging events. The proposed approach assumes a tight relation of mutual support between the modeling of individuals and groups, promoting the idea that groups are better modeled if individuals are considered and vice versa. This concept is translated in a mathematical model using a decentralized particle filtering framework which deals with a joint individual-group state space. The model factorizes the joint space into two dependent subspaces, where individuals and groups share the knowledge of the joint individual-group distribution. The assignment of people to the different groups (and thus group initialization, split and merge) is implemented by two alternative strategies: using classifiers trained beforehand on statistics of group configurations, and through online learning of a Dirichlet process mixture model, assuming that no training data is available before tracking. These strategies lead to two different methods that can be used on top of any person detector (simulated using the ground truth in our experiments). We provide convincing results on two recent challenging tracking benchmarks.
Learning how to teach from “Videolectures”: automatic prediction of lecture ratings based on teacher's nonverbal behavior

Large repositories of presentation recordings (e.g., “Videolectures” and “Academic Earth”) often provide their users with rating facilities. The rating of a presentation certainly depends on the content, but the way the content is delivered is likely to play a role as well. This paper focuses on the latter aspect and shows that nonverbal behavior (in particular arms movement and prosody) allows one to predict whether a presentation is rated as low or high in terms of quality. The experiments have been performed over 100 presentations collected from “Videolectures” and the accuracy is up to 66% depending on the techniques adopted. In other words, nonverbal communication actually influences the ratings assigned to a presentation.