nils-herrmann/clean_scirepeval huggingface dataset:
Efficient and Robust Learning for Sustainable and Reacquisition-Enabled Hand Tracking

The use of machine learning approaches for long-term hand tracking poses some major challenges such as attaining robustness to inconsistencies in lighting, scale and object appearances, background clutter, and total object occlusion/disappearance. To address these issues in this paper, we present a robust machine learning approach based on enhanced particle filter trackers. The inherent drawbacks associated with the particle filter approach, i.e., sample degeneration and sample impoverishment, are minimized by infusing the particle filter with the mean shift approach. Moreover, to instill our tracker with reacquisition ability, we propose a rotation invariant and efficient detection framework named beta histograms of oriented gradients. Our robust appearance model operates on the red, green, blue color histogram and our newly proposed rotation invariant noise compensated local binary patterns descriptor, which is a noise compensated, rotation invariant version of the local binary patterns descriptor. Through our experiments, we demonstrate that our proposed hand tracker performs favorably against state-of-the-art algorithms on numerous challenging video sequences of hand postures, and overcomes the largely unsolved problem of redetecting hands after they vanish and reappear into the frame.
Robust Web Image Annotation via Exploring Multi-Facet and Structural Knowledge

Driven by the rapid development of Internet and digital technologies, we have witnessed the explosive growth of Web images in recent years. Seeing that labels can reflect the semantic contents of the images, automatic image annotation, which can further facilitate the procedure of image semantic indexing, retrieval, and other image management tasks, has become one of the most crucial research directions in multimedia. Most of the existing annotation methods, heavily rely on well-labeled training data (expensive to collect) and/or single view of visual features (insufficient representative power). In this paper, inspired by the promising advance of feature engineering (e.g., CNN feature and scale-invariant feature transform feature) and inexhaustible image data (associated with noisy and incomplete labels) on the Web, we propose an effective and robust scheme, termed robust multi-view semi-supervised learning (RMSL), for facilitating image annotation task. Specifically, we exploit both labeled images and unlabeled images to uncover the intrinsic data structural information. Meanwhile, to comprehensively describe an individual datum, we take advantage of the correlated and complemental information derived from multiple facets of image data (i.e., multiple views or features). We devise a robust pairwise constraint on outcomes of different views to achieve annotation consistency. Furthermore, we integrate a robust classifier learning component via  $\ell _{2,p}$  loss, which can provide effective noise identification power during the learning process. Finally, we devise an efficient iterative algorithm to solve the optimization problem in RMSL. We conduct comprehensive experiments on three different data sets, and the results illustrate that our proposed approach is promising for automatic image annotation.
Multiple Auxiliaries Assisted Airborne Power Line Detection

Airborne power line detection is a key technique to ensure low altitude flight safety. Yet, it is a challenging problem due to the extremely small size of power line targets. Recently, auxiliary assisted detection has shown great potential in improving the power line detection performance. However, in existing methods, the auxiliaries and the contexts between the power lines and the auxiliaries are both manually assigned, thus limits its applicability. In this paper, a novel multiple auxiliaries assisted power line detection method is proposed. With an optimization based auxiliaries selection and contexts acquisition scheme, the proposed method cannot only decide which auxiliaries should be selected to assist the detection, but also acquire the context information of each kind of auxiliaries, all in an automatic way. Experimental results show that the proposed method surpasses the state-of-the-art power line detection methods, both in terms of detection accuracy and false alarm probability.
Fast Spectral Clustering with efficient large graph construction

Spectral clustering has been regarded as a powerful tool for unsupervised tasks despite its excellent performance, the high computational cost has become a bottleneck which limits its application for large scale problems. Recent studies on anchor-based graph can partly alleviate the problem, however, it is still a great challenge to deal with such data with both high performance and high efficiency. In this paper, we propose Fast Spectral Clustering (FSC) to efficiently deal with large scale data. The proposed method first constructs anchor-based similarity graph with Balanced K-means based Hierarchical K-means (BKHK) algorithm, and then performs spectral analysis on the graph. The overall computational complexity is O(ndm), where n is the number of samples, d is the number of features, and m is the number of anchors. Comprehensive experiments on several large scale data sets demonstrate the effectiveness and efficiency of the proposed method.
Invariant Image Watermarking Based on Local Feature Regions

In this paper, a robust image watermarking approach is presented based on image local invariant features. The affine invariant point detector is used to extract feature regions of the given host image. Image normalization and dominant gradient orientation alignment are performed to these feature regions. Thereafter, circular watermark pattern are embedded in these image normalized patches. After the above procedures, full affine invariance can be achieved over these normalized patches. A large amount of experiments demonstrate that the proposed approach is robust against different attacks, such as common image processing, rotation, scaling, cropping, and several other geometric distortions.
Low-Rank 2-D Neighborhood Preserving Projection for Enhanced Robust Image Representation

2-D neighborhood preserving projection (2DNPP) uses 2-D images as feature input instead of 1-D vectors used by neighborhood preserving projection (NPP). 2DNPP requires less computation time than NPP. However, both NPP and 2DNPP use the  ${L}_{ {{2}}}$  norm as a metric, which is sensitive to noise in data. In this paper, we proposed a novel NPP method called low-rank 2DNPP (LR-2DNPP). This method divided the input data into a component part that encoded low-rank features, and an error part that ensured the noise was sparse. Then, a nearest neighbor graph was learned from the clean data using the same procedure as 2DNPP. To ensure that the features learned by LR-2DNPP were optimal for classification, we combined the structurally incoherent learning and low-rank learning with NPP to form a unified model called discriminative LR-2DNPP (DLR-2DNPP). By encoding the structural incoherence of the learned clean data, DLR-2DNPP could enhance the discriminative ability for feature extraction. Theoretical analyses on the convergence and computational complexity of LR-2DNPP and DLR-2DNPP were presented in details. We used seven public image databases to verify the performance of the proposed methods. The experimental results showed the effectiveness of our methods for robust image representation.
Object-aware power line detection using color and near-infrared images

Vision-based power line detection (PLD) is an important yet challenging problem in low-altitude flight. Different from the traditional PLD methods, which only aim at line information, we propose a novel object-aware PLD method to obtain better detection performance. A new definition of power line is first proposed to present its object-aware properties; and then a cascaded PLD scheme is devised with line detection, region filtration, and object validation based on corresponding image cues in joint color (RGB) and near-infrared (NIR) images. Considering that the primary goal of PLD is to capture all potential information and decrease the false negatives, we first treat the universal line shape in pixel from joint RGB-NIR images as a basic feature to explore general line candidates. To further pick out the accurate regions occupied by power lines, on the one hand we filter the false candidates based on the region-based intensity of special material characteristics in NIR, and on the other hand we validate the power lines according to the color features in RGB. The experiments demonstrate the advantages of our proposed method in three aspects: good detection accuracy, high true detection rate, and low false detection rate.
A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering

Multiplicative update algorithms are important tools for information retrieval, image processing, and pattern recognition. However, when the graph regularization is added to the cost function, different classes of sample data may be mapped to the same subspace, which leads to the increase of data clustering error rate. In this paper, an improved nonnegative matrix factorization (NMF) cost function is introduced. Based on the cost function, a class of novel graph regularized NMF algorithms is developed, which results in a class of extended multiplicative update algorithms with manifold structure regularization. Analysis shows that in the learning, the proposed algorithms can efficiently minimize the rank of the data representation matrix. Theoretical results presented in this paper are confirmed by simulations. For different initializations and data sets, variation curves of cost functions and decomposition data are presented to show the convergence features of the proposed update rules. Basis images, reconstructed images, and clustering results are utilized to present the efficiency of the new algorithms. Last, the clustering accuracies of different algorithms are also investigated, which shows that the proposed algorithms can achieve state-of-the-art performance in applications of image clustering.
Putting poses on manifold for action recognition

In action recognition, bag of words based approaches have been shown to be successful, for which the quality of codebook is critical. This paper proposes a novel approach to select key poses for the codebook, which models the descriptor space utilizing manifold learning to recover the geometric structure of the descriptors on a lower dimensional manifold space. A PageRank based centrality measure is developed to select key poses on the manifold. In each step, a key pose is selected and the remaining model is modified to maximize the discriminative power of selected codebook. In classification, the ambiguity of each action couple is evaluated through cross validation. An additional subdivision will be executed for ambiguous pairs. Experiments on ut-tower dataset showed that our method is able to obtain better performance than the state-of-the-art methods.
Collaborative Kalman filters for vehicle tracking

Airborne vehicle tracking system is receiving increasing attention because of its high mobility and large surveillance scope. However, tracking multiple vehicles simultaneously on airborne platform is a challenging problem, owing to uncertain vehicle motion and visible frame-to-frame jitter caused by camera vibration. To address these problems, a new collaborative tracking framework is proposed. The framework consists of two level tracking processes: to track vehicles as groups, the higher level builds the relevance network and divides target vehicles into different groups; the relevance is calculated based on the status information of vehicles obtained by the lower level. This kind of group tracking takes into account the relevance of vehicles and reduces the impact of camera vibration, so the proposed method is applicable for multi-vehicle tracking in airborne videos. Experimental results demonstrate that the proposed method has better performance in terms of the tracking speed and accuracy compared to other existing approaches.
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization

Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real applications, such as image inpainting and recommender systems. Many existing approaches formulate this problem as a general low-rank matrix approximation problem. Since the rank operator is nonconvex and discontinuous, most of the recent theoretical studies use the nuclear norm as a convex relaxation. One major limitation of the existing approaches based on nuclear norm minimization is that all the singular values are simultaneously minimized, and thus the rank may not be well approximated in practice. In this paper, we propose to achieve a better approximation to the rank of matrix by truncated nuclear norm, which is given by the nuclear norm subtracted by the sum of the largest few singular values. In addition, we develop a novel matrix completion algorithm by minimizing the Truncated Nuclear Norm. We further develop three efficient iterative procedures, TNNR-ADMM, TNNR-APGL, and TNNR-ADMMAP, to solve the optimization problem. TNNR-ADMM utilizes the alternating direction method of multipliers (ADMM), while TNNR-AGPL applies the accelerated proximal gradient line search method (APGL) for the final optimization. For TNNR-ADMMAP, we make use of an adaptive penalty according to a novel update rule for ADMM to achieve a faster convergence rate. Our empirical study shows encouraging results of the proposed algorithms in comparison to the state-of-the-art matrix completion algorithms on both synthetic and real visual datasets.
On Combining Morphological Component Analysis and Concentric Morphology Model for Mammographic Mass Detection

Mammographic mass detection is an important task for the early diagnosis of breast cancer. However, it is difficult to distinguish masses from normal regions because of their abundant morphological characteristics and ambiguous margins. To improve the mass detection performance, it is essential to effectively preprocess mammogram to preserve both the intensity distribution and morphological characteristics of regions. In this paper, morphological component analysis is first introduced to decompose a mammogram into a piecewise-smooth component and a texture component. The former is utilized in our detection scheme as it effectively suppresses both structural noises and effects of blood vessels. Then, we propose two novel concentric layer criteria to detect different types of suspicious regions in a mammogram. The combination is evaluated based on the Digital Database for Screening Mammography, where 100 malignant cases and 50 benign cases are utilized. The sensitivity of the proposed scheme is 99% in malignant, 88% in benign, and 95.3% in all types of cases. The results show that the proposed detection scheme achieves satisfactory detection performance and preferable compromises between sensitivity and false positive rates.
Spectral Embedded Hashing for Scalable Image Retrieval

We propose a new graph based hashing method called spectral embedded hashing (SEH) for large-scale image retrieval. We first introduce a new regularizer into the objective function of the recent work spectral hashing to control the mismatch between the resultant hamming embedding and the low-dimensional data representation, which is obtained by using a linear regression function. This linear regression function can be employed to effectively handle the out-of-sample data, and the introduction of the new regularizer makes SEH better cope with the data sampled from a nonlinear manifold. Considering that SEH cannot efficiently cope with the high dimensional data, we further extend SEH to kernel SEH (KSEH) to improve the efficiency and effectiveness, in which a nonlinear regression function can also be employed to obtain the low dimensional data representation. We also develop a new method to efficiently solve the approximate solution for the eigenvalue decomposition problem in SEH and KSEH. Moreover, we show that some existing hashing methods are special cases of our KSEH. Our comprehensive experiments on CIFAR, Tiny-580K, NUS-WIDE, and Caltech-256 datasets clearly demonstrate the effectiveness of our methods.
Two-Stage Learning to Predict Human Eye Fixations via SDAEs

Saliency detection models aiming to quantitatively predict human eye-attended locations in the visual field have been receiving increasing research interest in recent years. Unlike traditional methods that rely on hand-designed features and contrast inference mechanisms, this paper proposes a novel framework to learn saliency detection models from raw image data using deep networks. The proposed framework mainly consists of two learning stages. At the first learning stage, we develop a stacked denoising autoencoder (SDAE) model to learn robust, representative features from raw image data under an unsupervised manner. The second learning stage aims to jointly learn optimal mechanisms to capture the intrinsic mutual patterns as the feature contrast and to integrate them for final saliency prediction. Given the input of pairs of a center patch and its surrounding patches represented by the features learned at the first stage, a SDAE network is trained under the supervision of eye fixation labels, which achieves both contrast inference and contrast integration simultaneously. Experiments on three publically available eye tracking benchmarks and the comparisons with 16 state-of-the-art approaches demonstrate the effectiveness of the proposed framework.
When Collaboration Hugs Intelligence: Content Delivery over Ultra-Dense Networks

With the increasing popularity of traffic-intensive video applications, UDNs are treated as one of the most promising technologies for massive video delivery. However, due to the drastic interference between neighboring cells, how to achieve high energy and spectrum efficiency is still an open and challenging problem. This work proposes a systematic solution for content delivery over UDNs by integrating collaboration with intelligence. In particular, we first design a hybrid video coding scheme that is flexible and robust to the dynamic wireless environment. Then an active and proactive video update strategy is designed by intelligently alleviating the impact of the interference. Finally, a collaborative video scheduling scheme is developed to maximize the video quality as well as the energy and spectrum efficiency. Importantly, we summarize three fundamental design guidelines, and believe that they are useful for improving the transmission capacity of UDNs.
Efficient Feature Selection via $\ell _{2, 0}$ℓ2, 0-norm Constrained Sparse Regression

Sparse regression based feature selection method has been extensively investigated these years. However, because it has a non-convex constraint, i.e., $\ell _{2,0}$l2,0-norm constraint, this problem is very hard to solve. In this paper, unlike most of the other methods which only solve its slack version by introducing sparsity regularization into objective function forcibly, a novel framework is proposed by us to solve the original $\ell _{2,0}$l2,0-norm constrained sparse regression based feature selection problem. We transform our objective function into Linear Discriminant Analysis (LDA) by using a new label coding method, thus enabling our model to calculate the ratio of inter-class scatter to intra-class scatter of features which is the most widely used feature discrimination evaluation metric. According to that ratio, features can be selected by a simple sorting method. The projection gradient descent algorithm is introduced to further improve the performance of our algorithm by using the solution obtained before as its initial solution. This ensures the stability of this iterative algorithm. We prove that the proposed method can get the global optimal solution of this non-convex problem when all features are statistically independent. For the general case where features are statistically dependent, extensive experiments on six small sample size datasets and one large-scale dataset show that our algorithm has comparable or better classification capability comparing with other eight state-of-the-art feature selection methods by the SVM classifier. We also show that our algorithm can obtain a low loss value, which means the solution of our algorithm can get very close to this NP-hard problem’s real solution. What is more, because we solve the original $\ell _{2,0}$l2,0-norm constrained problem, we avoid the heavy work of tuning the regularization parameter because its meaning is explicit in our method, i.e., the number of selected features. At last, we evaluate the stability of our algorithm from two perspectives, i.e., the objective function values and the selected features, by experiments. From both perspectives, our algorithm shows satisfactory stability performance.
Learning shape statistics for hierarchical 3D medical image segmentation

Accurate image segmentation is important for many medical imaging applications, whereas it remains challenging due to the complexity in medical images, such as the complex shapes and varied neighbor structures. This paper proposes a new hierarchical 3D image segmentation method based on patient-specific shape prior and surface patch shape statistics (SURPASS) model. In the segmentation process, a coarse-to-fine, two-stage strategy is designed, which contains global segmentation and local segmentation. In the global segmentation stage, patient-specific shape prior is estimated by using manifold learning techniques to achieve the overall segmentation. In the second stage, SURPASS is computed to solve the problem of poor segmentation at certain surface patches. The effectiveness of the proposed 3D image segmentation method has been demonstrated by the experiments on segmenting the prostate from a series of MR images.
Selecting Key Poses on Manifold for Pairwise Action Recognition

In action recognition, bag of visual words based approaches have been shown to be successful, for which the quality of codebook is critical. In a large vocabulary of poses (visual words), some key poses play a more decisive role than others in the codebook. This paper proposes a novel approach for key poses selection, which models the descriptor space utilizing a manifold learning technique to recover the geometric structure of the descriptors on a lower dimensional manifold. A PageRank-based centrality measure is developed to select key poses according to the recovered geometric structure. In each step, a key pose is selected from the manifold and the remaining model is modified to maximize the discriminative power of selected codebook. With the obtained codebook, each action can be represented with a histogram of the key poses. To solve the ambiguity between some action classes, a pairwise subdivision is executed to select discriminative codebooks for further recognition. Experiments on benchmark datasets showed that our method is able to obtain better performance compared with other state-of-the-art methods.