nils-herrmann/clean_scirepeval huggingface dataset:
Connected vehicle safety science, system, and framework

In this paper, we propose a framework to develop an M2M-based (machine-to-machine) proactive driver assistance system. Unlike traditional approaches, we take the benefits of M2M in intelligent transportation system (ITS): 1) expansion of sensor coverage, 2) increase of time allowed to react, and 3) mediation of bidding for right of way, to help driver avoiding potential traffic accidents. To develop such a system, we divide it into three main parts: 1) driver behavior modeling and prediction, which collects grand driving data to learn and predict the future behaviors of drivers; 2) M2M-based neighbor map building, which includes sensing, communication, and fusion technologies to build a neighbor map, where neighbor map mentions the locations of all neighboring vehicles; 3) design of passive information visualization and proactive warning mechanism, which researches on how to provide user-needed information and warning signals to drivers without interfering their driving activities.
Constraint-based feature recognition: handling non-uniqueness in feature interactions

The feature recognition problem is formulated as a geometric constraint satisfaction problem (GCSP), in which variables are the faces in the delta volume, the possible feature classifications determine values for the variables, and the constraints are the geometric and topological properties between the variables. A novel representation language is proposed to encode more, and somewhat redundant, information about geometric and topological properties in form features. The redundant information adds more constraints amongst objects in feature interactions and thus helps in feature recognition. Moreover, the language can be extended to represent new features.
Multi-instance object segmentation with occlusion handling

We present a multi-instance object segmentation algorithm to tackle occlusions. As an object is split into two parts by an occluder, it is nearly impossible to group the two separate regions into an instance by purely bottomup schemes. To address this problem, we propose to incorporate top-down category specific reasoning and shape prediction through exemplars into an intuitive energy minimization framework. We perform extensive evaluations of our method on the challenging PASCAL VOC 2012 segmentation set. The proposed algorithm achieves favorable results on the joint detection and segmentation task against the state-of-the-art method both quantitatively and qualitatively.
Interactive Cosegmentation Using Global and Local Energy Optimization

We propose a novel interactive cosegmentation method using global and local energy optimization. The global energy includes two terms: 1) the global scribbled energy and 2) the interimage energy. The first one utilizes the user scribbles to build the Gaussian mixture model and improve the cosegmentation performance. The second one is a global constraint, which attempts to match the histograms of common objects. To minimize the local energy, we apply the spline regression to learn the smoothness in a local neighborhood. This energy optimization can be converted into a constrained quadratic programming problem. To reduce the computational complexity, we propose an iterative optimization algorithm to decompose this optimization problem into several subproblems. The experimental results show that our method outperforms the state-of-the-art unsupervised cosegmentation and interactive cosegmentation methods on the iCoseg and MSRC benchmark data sets.
A New Image Quality Metric for Image Auto-denoising

This paper proposes a new non-reference image quality metric that can be adopted by the state-of-the-art image/ video denoising algorithms for auto-denoising. The proposed metric is extremely simple and can be implemented in four lines of Matlab code. The basic assumption employed by the proposed metric is that the noise should be independent of the original image. A direct measurement of this dependence is, however, impractical due to the relatively low accuracy of existing denoising method. The proposed metric thus aims at maximizing the structure similarity between the input noisy image and the estimated image noise around homogeneous regions and the structure similarity between the input noisy image and the denoised image around highly-structured regions, and is computed as the linear correlation coefficient of the two corresponding structure similarity maps. Numerous experimental results demonstrate that the proposed metric not only outperforms the current state-of-the-art non-reference quality metric quantitatively and qualitatively, but also better maintains temporal coherence when used for video denoising.
Dual Deep Network for Visual Tracking

Visual tracking addresses the problem of identifying and localizing an unknown target in a video given the target specified by a bounding box in the first frame. In this paper, we propose a dual network to better utilize features among layers for visual tracking. It is observed that features in higher layers encode semantic context while its counterparts in lower layers are sensitive to discriminative appearance. Thus we exploit the hierarchical features in different layers of a deep model and design a dual structure to obtain better feature representation from various streams, which is rarely investigated in previous work. To highlight geometric contours of the target, we integrate the hierarchical feature maps with an edge detector as the coarse prior maps to further embed local details around the target. To leverage the robustness of our dual network, we train it with random patches measuring the similarities between the network activation and target appearance, which serves as a regularization to enforce the dual network to focus on target object. The proposed dual network is updated online in a unique manner based on the observation, that the target being tracked in consecutive frames should share more similar feature representations than those in the surrounding background. It is also found that for a target object, the prior maps can help further enhance performance by passing message into the output maps of the dual network. Therefore, an independent component analysis with reference algorithm is employed to extract target context using prior maps as guidance. Online tracking is conducted by maximizing the posterior estimate on the final maps with stochastic and periodic update. Quantitative and qualitative evaluations on two large-scale benchmark data sets show that the proposed algorithm performs favorably against the state-of-the-arts.
Ranking Saliency

Most existing bottom-up algorithms measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead of only considering the contrast between salient objects and their surrounding regions, we consider both foreground and background cues in this work. We rank the similarity of image elements with foreground or background cues via graph-based manifold ranking. The saliency of image elements is defined based on their relevances to the given seeds or queries. We represent an image as a multi-scale graph with fine superpixels and coarse regions as nodes. These nodes are ranked based on the similarity to background and foreground queries using affinity matrices. Saliency detection is carried out in a cascade scheme to extract background regions and foreground salient objects efficiently. Experimental results demonstrate the proposed method performs well against the state-of-the-art methods in terms of accuracy and speed. We also propose a new benchmark dataset containing 5,168 images for large-scale performance evaluation of saliency detection methods.
A New Affine Registration Algorithm for Matching 2D Point Sets

We propose a novel affine registration algorithm for matching 2D feature points. Unlike many previously published work on affine point matching, the proposed algorithm does not require any optimization and in the absence of data noise, the algorithm will recover the exact affine transformation and the unknown correspondence. The two-step algorithm first reduces the general affine case to the orthogonal case, and the unknown rotation is computed as the roots of a low-degree polynomial with complex coefficients. The algebraic and geometric ideas behind the proposed method are both clear and transparent, and its implementation is straightforward. We validate the algorithm on a variety of synthetic 2D point sets as well as feature points on images of real-world objects
Pedestrian detection in crowded scenes via scale and occlusion analysis

Despite significant progress in pedestrian detection has been made in recent years, detecting pedestrians in crowded scenes remains a challenging problem. In this paper, we propose to use visual contexts based on scale and occlusion cues from detections at proximity to better detect pedestrians for surveillance applications. Specifically, we first apply detectors based on full body and parts to generate initial detections. Scale prior at each image location is estimated using the cues provided by neighboring detections, and the confidence score of each detection is refined according to its consistency with the estimated scale prior. Local occlusion analysis is exploited in refining detection confidence scores which facilitates the final detection cluster based Non-Maximum Suppression. Experimental results on benchmark data sets show that the proposed algorithm performs favorably against the state-of-the-art methods.
A natural language processing based Internet agent

Searching for useful information is a difficult job by the virtue of the information overloading problem. With technological advances, notably the World-Wide Web (WWW), it allows every ordinary information owner to offer information online for others to access and retrieve. However, it also makes up a global information system that is extremely large-scale, diverse and dynamic. Internet agents and Internet search engines have been used to deal with such problems. But the search results are usually not quite relevant to what a user wants since most of them use simple keyword matching. In this paper, we propose a natural language processing based agent (NIAGENT) that understands a user's natural query. NIAGENT not only cooperates with a meta Internet search engine in order to increase recall of web pages but also analyzes the contents of the referenced documents to increase precision. Moreover, the proposed agent is autonomous, light-weight, and multithreaded. The architectural design also represents an interesting application of a distributed and cooperative computing paradigm. A prototype of NIAGENT, implemented in Java, shows its promise to find more useful information than keyword based searching.
Locality preserving hashing

The spectral hashing algorithm relaxes and solves an objective function for generating hash codes such that data similarity is preserved in the Hamming space. However, the assumption of uniform global data distribution limits its applicability. In the paper, we introduce locality preserving projection to determine the data distribution adaptively, and a spectral method is adopted to estimate the eigenfunctions of the underlying graph Laplacian. Furthermore, pairwise label similarity can be further incorporated in the weight matrix to bridge the semantic gap between data and hash codes. Experiments on three benchmark datasets show the proposed algorithm performs favorably against state-of-the-art hashing methods.
Video Segmentation via Object Flow

Video object segmentation is challenging due to fast moving objects, deforming shapes, and cluttered backgrounds. Optical flow can be used to propagate an object segmentation over time but, unfortunately, flow is often inaccurate, particularly around object boundaries. Such boundaries are precisely where we want our segmentation to be accurate. To obtain accurate segmentation across time, we propose an efficient algorithm that considers video segmentation and optical flow estimation simultaneously. For video segmentation, we formulate a principled, multiscale, spatio-temporal objective function that uses optical flow to propagate information between frames. For optical flow estimation, particularly at object boundaries, we compute the flow independently in the segmented regions and recompose the results. We call the process object flow and demonstrate the effectiveness of jointly optimizing optical flow and video segmentation using an iterative scheme. Experiments on the SegTrack v2 and Youtube-Objects datasets show that the proposed algorithm performs favorably against the other state-of-the-art methods.
PatchCut: Data-driven object segmentation via local shape transfer

Object segmentation is highly desirable for image understanding and editing. Current interactive tools require a great deal of user effort while automatic methods are usually limited to images of special object categories or with high color contrast. In this paper, we propose a data-driven algorithm that uses examples to break through these limits. As similar objects tend to share similar local shapes, we match query image patches with example images in multiscale to enable local shape transfer. The transferred local shape masks constitute a patch-level segmentation solution space and we thus develop a novel cascade algorithm, PatchCut, for coarse-to-fine object segmentation. In each stage of the cascade, local shape mask candidates are selected to refine the estimated segmentation of the previous stage iteratively with color models. Experimental results on various datasets (Weizmann Horse, Fashionista, Object Discovery and PASCAL) demonstrate the effectiveness and robustness of our algorithm.
Long-term correlation tracking

In this paper, we address the problem of long-term visual tracking where the target objects undergo significant appearance variation due to deformation, abrupt motion, heavy occlusion and out-of-view. In this setting, we decompose the task of tracking into translation and scale estimation of objects. We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn discriminative correlation filters from the most confident frames to estimate the scale change. In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. Extensive experimental results on large-scale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy, and robustness.
Single image deblurring with adaptive dictionary learning

We propose a motion deblurring algorithm that exploits sparsity constraints of image patches using one single frame. In our formulation, each image patch is encoded with sparse coefficients using an over-complete dictionary. The sparsity constraints facilitate recovering the latent image without solving an ill-posed deconvolution problem. In addition, the dictionary is learned and updated directly from one single frame without using additional images. The proposed method iteratively utilizes sparsity constraints to recover latent image, estimates the deblur kernel, and updates the dictionary directly from one single image. The final deblurred image is then recovered once the deblur kernel is estimated using our method. Experiments show that the proposed algorithm achieves favorable results against the state-of-the-art methods.
Introduction to the Special Section on Real-World Face Recognition

The motivations for organizing this special section were to better address the challenges of face recognition in real-world scenarios, to promote systematic research and evaluation of promising methods and systems, to provide a snapshot of where we are in this domain, and to stimulate discussion about future directions. We solicited original contributions of research on all aspects of real-world face recognition, including: the design of robust face similarity features and metrics; robust face clustering and sorting algorithms; novel user interaction models and face recognition algorithms for face tagging; novel applications of web face recognition; novel computational paradigms for face recognition; challenges in large scale face recognition tasks, e.g., on the Internet; face recognition with contextual information; face recognition benchmarks and evaluation methodology for moderately controlled or uncontrolled environments; and video face recognition. We received 42 original submissions, four of which were rejected without review; the other 38 papers entered the normal review process. Each paper was reviewed by three reviewers who are experts in their respective topics. More than 100 expert reviewers have been involved in the review process. The papers were equally distributed among the guest editors. A final decision for each paper was made by at least two guest editors assigned to it. To avoid conflict of interest, no guest editor submitted any papers to this special section.
Deblurring Text Images via L0-Regularized Intensity and Gradient Prior

We propose a simple yet effective L0-regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is motivated by observing distinct properties of text images. Based on this prior, we develop an efficient optimization method to generate reliable intermediate results for kernel estimation. The proposed method does not require any complex filtering strategies to select salient edges which are critical to the state-of-the-art deblurring algorithms. We discuss the relationship with other deblurring algorithms based on edge selection and provide insight on how to select salient edges in a more principled way. In the final latent image restoration step, we develop a simple method to remove artifacts and render better deblurred images. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art text image deblurring methods. In addition, we show that the proposed method can be effectively applied to deblur low-illumination images.
Fast Human Pose Estimation using Appearance and Motion via Multi-Dimensional Boosting Regression

We address the problem of estimating human pose in video sequences, where rough location has been determined. We exploit both appearance and motion information by defining suitable features of an image and its temporal neighbors, and learning a regression map to the parameters of a model of the human body using boosting techniques. Our algorithm can be viewed as a fast initialization step for human body trackers, or as a tracker itself. We extend gradient boosting techniques to learn a multi-dimensional map from (rotated and scaled) Haar features to the entire set of joint angles representing the full body pose. We test our approach by learning a map from image patches to body joint angles from synchronized video and motion capture walking data. We show how our technique enables learning an efficient real-time pose estimator, validated on publicly available datasets.
Learning Support Correlation Filters for Visual Tracking

For visual tracking methods based on kernel support vector machines (SVMs), data sampling is usually adopted to reduce the computational cost in training. In addition, budgeting of support vectors is required for computational efficiency. Instead of sampling and budgeting, recently the circulant matrix formed by dense sampling of translated image patches has been utilized in kernel correlation filters for fast tracking. In this paper, we derive an equivalent formulation of a SVM model with the circulant matrix expression and present an efficient alternating optimization method for visual tracking. We incorporate the discrete Fourier transform with the proposed alternating optimization process, and pose the tracking problem as an iterative learning of support correlation filters (SCFs). In the fully-supervision setting, our SCF can find the globally optimal solution with real-time performance. For a given circulant data matrix with <inline-formula><tex-math notation="LaTeX">$n^2$</tex-math><alternatives><mml:math><mml:msup><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq1-2829180.gif"/></alternatives></inline-formula> samples of <inline-formula><tex-math notation="LaTeX">$n \times n$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>n</mml:mi><mml:mo>×</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq2-2829180.gif"/></alternatives></inline-formula> pixels, the computational complexity of the proposed algorithm is <inline-formula><tex-math notation="LaTeX">$O(n^2\; \log n)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mspace width="0.277778em"/><mml:mo form="prefix">log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq3-2829180.gif"/></alternatives></inline-formula> whereas that of the standard SVM-based approaches is at least <inline-formula><tex-math notation="LaTeX">$O(n^4)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mn>4</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq4-2829180.gif"/></alternatives></inline-formula>. In addition, we extend the SCF-based tracking algorithm with multi-channel features, kernel functions, and scale-adaptive approaches to further improve the tracking performance. Experimental results on a large benchmark dataset show that the proposed SCF-based algorithms perform favorably against the state-of-the-art tracking methods in terms of accuracy and speed.
Exploiting Spatial-Temporal Locality of Tracking via Structured Dictionary Learning.

In this paper, a novel spatial-temporal locality is proposed and unified via a discriminative dictionary learning framework for visual tracking. By exploring the strong local correlations between temporally obtained target and their spatially distributed nearby background neighbors, a spatial-temporal locality is obtained. The locality is formulated as a subspace model and exploited under a unified structure of discriminative dictionary learning with a subspace structure. Using the learned dictionary, the target and its background can be described and distinguished effectively through their sparse codes. As a result, the target is localized by integrating both the descriptive and the discriminative qualities. Extensive experiments on various challenging video sequences demonstrate the superior performance of proposed algorithm over the other state-of-the-art approaches.
NUS-PRO: A New Visual Tracking Challenge

Numerous approaches on object tracking have been proposed during the past decade with demonstrated success. However, most tracking algorithms are evaluated on limited video sequences and annotations. For thorough performance evaluation, we propose a large-scale database which contains 365 challenging image sequences of pedestrians and rigid objects. The database covers 12 kinds of objects, and most of the sequences are captured from moving cameras. Each sequence is annotated with target location and occlusion level for evaluation. A thorough experimental evaluation of 20 state-of-the-art tracking algorithms is presented with detailed analysis using different metrics. The database is publicly available and evaluation can be carried out online for fair assessments of visual tracking algorithms.