nils-herrmann/clean_scirepeval huggingface dataset:
Detecting risk-markers in children in a preschool classroom

Early intervention in mental disorders can dramatically increase an individual's quality of life. Additionally, when symptoms of mental illness appear in childhood or adolescence, they represent the later stages of a process that began years earlier. One goal of psychiatric research is to identify risk-markers: genetic, neural, behavioral and/or social deviations that indicate elevated risk of a particular mental disorder. Ideally, screening of risk-markers should occur in a community setting, and not a clinical setting which may be time-consuming and resource-intensive. Given this situation, a system for automatically detecting risk-markers in children would be highly valuable. In this paper, we describe such a system that has been installed at the Shirley G. Moore Lab School, a research pre-school at the University of Minnesota. This system consists of multiple RGB+D sensors and is able to detect children and adults in the classroom, tracking them as they move around the room. We use the tracking results to extract high-level information about the behavior and social interaction of children, that can then be used to screen for early signs of mental disorders.
Visualization of high dynamic range images

A novel paradigm for information visualization in high dynamic range images is presented. These images, real or synthetic, have luminance with typical ranges many orders of magnitude higher than that of standard output devices, thereby requiring some processing for their visualization. In contrast to existing approaches, which compute a single image with reduced range, close in a given sense to the original data, we propose to look for a representative set of images. The goal is then to produce a minimal set of images capturing the information all over the high dynamic range data, while at the same time preserving a natural appearance for each one of the images in the set. A specific algorithm that achieves this goal is presented and tested on natural and synthetic data.
A computer vision approach for the assessment of autism-related behavioral markers

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child's natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by facial feature tracking, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician's behavioral observations obtained from real in-clinic assessments.
Deep Video Deblurring for Hand-Held Cameras

Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on the alignment of nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods that aggregate information must therefore be able to identify which regions have been accurately aligned and which have not, a task that requires high level scene understanding. In this work, we introduce a deep learning solution to video deblurring, where a CNN is trained end-to-end to learn how to accumulate information across frames. To train this network, we collected a dataset of real videos recorded with a high frame rate camera, which we use to generate synthetic motion blur for supervision. We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.
See all by looking at a few: Sparse modeling for finding representative objects

We consider the problem of finding a few representatives for a dataset, i.e., a subset of data points that efficiently describes the entire dataset. We assume that each data point can be expressed as a linear combination of the representatives and formulate the problem of finding the representatives as a sparse multiple measurement vector problem. In our formulation, both the dictionary and the measurements are given by the data matrix, and the unknown sparse codes select the representatives via convex optimization. In general, we do not assume that the data are low-rank or distributed around cluster centers. When the data do come from a collection of low-rank models, we show that our method automatically selects a few representatives from each low-rank model. We also analyze the geometry of the representatives and discuss their relationship to the vertices of the convex hull of the data. We show that our framework can be extended to detect and reject outliers in datasets, and to efficiently deal with new observations and large datasets. The proposed framework and theoretical foundations are illustrated with examples in video summarization and image classification using representatives.
Locating occupants in preschool classrooms using a multiple RGB-D sensor system

Presented are results demonstrating that, in developing a system with its first objective being the sustained detection of adults and young children as they move and interact in a normal preschool setting, the direct application of the straightforward RGB-D innovations presented here significantly outperforms even far more algorithmically advanced methods relying solely on images. The use of multiple RGB-D sensors by this project for depth-aware object localization economically resolves numerous issues regularly frustrating earlier vision-only detection and human surveillance methods, issues such as occlusions, illumination changes, unexpected postures, atypical morphologies, erratic or unanticipated motions, reflections, and misleading textures and colorations. This multiple RGB-D installation forms the front-end for a multi-step pipeline, the first portion of which seeks to isolate, in situ, 3D renderings of classroom occupants sufficient for a later analysis of their behaviors and interactions. Towards this end, a voxel-based approach to foreground/background separation and an effective adaptation of supervoxel clustering for 3D were developed, and 3D and image-only methods were tested and compared. The project's setting is highly challenging, but then so are its longer term goals: the automated detection of early childhood precursors, ofttimes very subtle, to a number of increasingly common developmental disorders.
Clinical subthalamic nucleus prediction from high-field brain MRI

The subthalamic nucleus (STN) within the sub-cortical region of the Basal ganglia is a crucial targeting structure for Parkinson's Deep brain stimulation (DBS) surgery. Volumetric segmentation of such small and complex structure, which is elusive in clinical MRI protocols, is thereby a pre-requisite process for reliable DBS direct targeting. While direct visualization of the STN is facilitated with advanced ultrahigh-field MR imaging (7 Tesla), such high fields are not always clinically available. In this paper, we aim at the automatic prediction of the STN region on clinical low-field MRI, exploiting dependencies between the STN and its adjacent structures, learned from ultrahigh-field MRI. We present a framework based on a statistical shape model to learn such shape relationship on high quality MR data sets. This allows for an accurate prediction and visualization of the STN structure, given detectable predictors on the low-field MRI. Experimental results on Parkinson's patients demonstrate that the proposed approach enables accurate estimation of the STN on clinical 1.5T MRI.
Automatic colon polyp flagging via geometric and texture features

Computer Tomographic Colonography, combined with computer-aided detection (CAD), is a promising emerging technique for colonic polyp analysis. We present a CAD scheme for polyp flagging based on new texture and geometric features that consider both the information in the candidate polyp location and its immediate surrounding area, testing multiple sizes. The proposed algorithm is tested with ground truth data, including flat and small polyps, with very promising results.
Removing Camera Shake via Weighted Fourier Burst Accumulation

Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent inverse problem. The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method can be seen as a generalization of the align and average procedure, with a weighted average, motivated by hand-shake physiology and theoretically supported, taking place in the Fourier domain. The method's rationale is that camera shake has a random nature, and therefore, each image in the burst is generally blurred differently. Experiments with real camera data, and extensive comparisons, show that the proposed Fourier burst accumulation algorithm achieves state-of-the-art results an order of magnitude faster, with simplicity for on-board implementation on camera phones. Finally, we also present experiments in real high dynamic range (HDR) scenes, showing how the method can be straightforwardly extended to HDR photography.
LOw-rank data modeling via the minimum description length principle

Robust low-rank matrix estimation is a topic of increasing interest, with promising applications in a variety of fields, from computer vision to data mining and recommender systems. Recent theoretical results establish the ability of such data models to recover the true underlying low-rank matrix when a large portion of the measured matrix is either missing or arbitrarily corrupted. However, if low rank is not a hypothesis about the true nature of the data, but a device for extracting regularity from it, no current guidelines exist for choosing the rank of the estimated matrix. In this work we address this problem by means of the Minimum Description Length (MDL) principle - a well established information-theoretic approach to statistical inference - as a guideline for selecting a model for the data at hand. We demonstrate the practical usefulness of our formal approach with results for complex background extraction in video sequences.
Learning compressed image classification features

Learning a transformation-based dimension reduction, thereby compressive, technique for classification is here proposed. High-dimensional data often approximately lie in a union of low-dimensional subspaces. We propose to perform dimension reduction by learning a “fat” linear transformation matrix on subspaces using nuclear norm as the optimization criteria. The learned transformation enables dimension reduction, and, at the same time, restores a low-rank structure for data from the same class and maximizes the separation between different classes, thereby improving classification via learned low-dimensional features. Theoretical and experimental results support the proposed framework, which can be interpreted as learning compressing sensing matrices for classification.
Collaborative sources identification in mixed signals via hierarchical sparse modeling

A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on C-HiLasso, a convex collaborative hierarchical sparse model, and proceeds as follows. First, we build a structured dictionary for mixed signals by concatenating a set of sub-dictionaries, each one of them learned to sparsely model one of a set of possible classes. Then, the coding of the mixed signal is performed by efficiently solving a convex optimization problem that combines standard sparsity with group and collaborative sparsity. The present sources are identified by looking at the sub-dictionaries automatically selected in the coding. The collaborative filtering in C-HiLasso takes advantage of the temporal/spatial redundancy in the mixed signals, letting collections of samples collaborate in identifying the classes, while allowing individual samples to have different internal sparse representations. This collaboration is critical to further stabilize the sparse representation of signals, in particular the class/sub-dictionary selection. The internal sparsity inside the sub-dictionaries, as naturally incorporated by the hierarchical aspects of C-HiLasso, is critical to make the model consistent with the essence of the sub-dictionaries that have been trained for sparse representation of each individual class. We present applications from speaker and instrument identification and texture separation. In the case of audio signals, we use sparse modeling to describe the short-term power spectrum envelopes of harmonic sounds. The proposed pitch independent method automatically detects the number of sources on a recording.
An MDL Framework for Sparse Coding and Dictionary Learning

The power of sparse signal modeling with learned overcomplete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical inference and machine learning. However, the statistical properties of these models, such as underfitting or overfitting given sets of data, are still not well characterized in the literature. As a result, the success of sparse modeling depends on hand-tuning critical parameters for each data and application. This work aims at addressing this by providing a practical and objective characterization of sparse models by means of the minimum description length (MDL) principle-a well-established information-theoretic approach to model selection in statistical inference. The resulting framework derives a family of efficient sparse coding and dictionary learning algorithms which, by virtue of the MDL principle, are completely parameter free. Furthermore, such framework allows to incorporate additional prior information to existing models, such as Markovian dependencies, or to define completely new problem formulations, including in the matrix analysis area, in a natural way. These virtues will be demonstrated with parameter-free algorithms for the classic image denoising and classification problems, and for low-rank matrix recovery in video applications. However, the framework is not limited to this imaging data, and can be applied to a wide range of signal and data types and tasks.
Connecting the Out-of-Sample and Pre-Image Problems in Kernel Methods

Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more appropriate for analysis. Many manifold learning and dimensionality reduction techniques are simply kernel methods for which the mapping is explicitly computed. In such cases, two problems related with the mapping arise: The out-of-sample extension and the pre-image computation. In this paper we propose a new pre-image method based on the Nystrom formulation for the out-of-sample extension, showing the connections between both problems. We also address the importance of normalization in the feature space, which has been ignored by standard pre-image algorithms. As an example, we apply these ideas to the Gaussian kernel, and relate our approach to other popular pre-image methods. Finally, we show the application of these techniques in the study of dynamic shapes.
Dictionary learning and sparse coding for unsupervised clustering

A clustering framework within the sparse modeling and dictionary learning setting is introduced in this work. Instead of searching for the set of centroid that best fit the data, as in k-means type of approaches that model the data as distributions around discrete points, we optimize for a set of dictionaries, one for each cluster, for which the signals are best reconstructed in a sparse coding manner. Thereby, we are modeling the data as the of union of learned low dimensional subspaces, and data points associated to subspaces spanned by just a few atoms of the same learned dictionary are clustered together. Using learned dictionaries makes this method robust and well suited to handle large datasets. The proposed clustering algorithm uses a novel measurement for the quality of the sparse representation, inspired by the robustness of the ℓ1 regularization term in sparse coding. We first illustrate this measurement with examples on standard image and speech datasets in the supervised classification setting, showing with a simple approach its discriminative power and obtaining results comparable to the state-of-the-art. We then conclude with experiments for fully unsupervised clustering on extended standard datasets and texture images, obtaining excellent performance.
Learning Transformations

A low-rank transformation learning framework for subspace clustering and classification is here proposed. Many high-dimensional data, such as face images and motion sequences, approximately lie in a union of low-dimensional subspaces. The corresponding subspace clustering problem has been extensively studied in the literature, partitioning such high-dimensional data into clusters corresponding to their underlying low-dimensional subspaces. However, low-dimensional intrinsic structures are often violated for real-world observations, as they can be corrupted by errors or deviate from ideal models. We propose to address this by learning a linear transformation on subspaces using matrix rank, via its convex surrogate nuclear norm, as the optimization criteria. The learned linear transformation restores a low-rank structure for data from the same subspace, and, at the same time, forces a high-rank structure for data from different subspaces. In this way, we reduce variations within the subspaces, and increase separation between the subspaces for improved subspace clustering and classification.
Super-resolution texturing for online virtual globes

Online virtual globe applications such as Google Earth and Maps, Microsoft Virtual Earth, and Yahoo! Maps, allow users to explore realistic models of the Earth. To provide the ground-level detail of interest to users, it is necessary to serve and render high resolution images. For planetary coverage at high resolution, a very large number of images need to be acquired, stored, and transmitted, with consequent high costs and difficulty for the application provider, often resulting in lower than expected performance. In this work we propose a supplementary approach to render appropriate visual information in these applications. Using super-resolution techniques based on the combination and extension of known texture transfer and synthesis algorithms, we develop a system to efficiently synthesize fine detail consistent with the textures served. This approach dramatically reduces the operational cost of virtual globe displays, which are among the most image-intensive applications on the Internet, while at the same time improving their appearance. The proposed framework is fast and preserves the coherence between corresponding images at different resolutions, allowing consistent and responsive interactive zooming and panning operations. The framework is capable of adapting a library of multiscale textures to pre-segmented regions in the highest-resolution texture maps available. We also describe a simple interface to obtain class label information from contributing users. The presentation of the constituent techniques is complemented with examples simulating our framework embedded in Google Earth.
Adaptive temporal compressive sensing for video

This paper introduces the concept of adaptive temporal compressive sensing (CS) for video. We propose a CS algorithm to adapt the compression ratio based on the scene's temporal complexity, computed from the compressed data, without compromising the quality of the reconstructed video. The temporal adaptivity is manifested by manipulating the integration time of the camera, opening the possibility to realtime implementation. The proposed algorithm is a generalized temporal CS approach that can be incorporated with a diverse set of existing hardware systems.
All for one, one for all: Consensus community detection in networks

Given an universe of distinct, low-level communities of a network, we aim at identifying the “meaningful” and consistent communities in this universe. We address this as the process of obtaining consensual community detections and formalize it as a bi-clustering problem. While most consensus algorithms only take into account pairwise relations and end up analyzing a huge matrix, our proposed characterization of the consensus problem (1) does not drop useful information, and (2) analyzes a much smaller matrix, rendering the problem tractable for large networks. We also propose a new parameterless bi-clustering algorithm, fit for the type of matrices we analyze. The approach has proven successful in a very diverse set of experiments, ranging from unifying the results of multiple community detection algorithms to finding common communities from multi-modal or noisy networks.
Interactive Image Segmentation via Adaptive Weighted Distances

An interactive algorithm for soft segmentation of natural images is presented in this paper. The user first roughly scribbles different regions of interest, and from them, the whole image is automatically segmented. This soft segmentation is obtained via fast, linear complexity computation of weighted distances to the user-provided scribbles. The adaptive weights are obtained from a series of Gabor filters, and are automatically computed according to the ability of each single filter to discriminate between the selected regions of interest. We present the underlying framework and examples showing the capability of the algorithm to segment diverse images