nils-herrmann/clean_scirepeval huggingface dataset:
Ranking social emotions by learning listwise preference

Emotion modeling has received a great attention in recent years. This paper models the online social emotions that are the online users' emotional responds when they are exposed to news articles. Specifically, we rank social emotion labels for online documents. Unlike the existing method, referred to as Pair-LR, which learns pairwise preference and adopts binary classification, we address the problem of ranking social emotions by learning listwise preference. In particular, a novel approach, referred to as List-LR, is proposed to learn a ranking model for social emotion labels of online documents by minimizing the listwise loss defined on instances. Empirical experiments show that the proposed approach outperforms Pair-LR and is also competitive to other two start-of-the-art approaches for label ranking.
Towards Multi-view and Partially-Occluded Face Alignment

We present a robust model to locate facial landmarks under different views and possibly severe occlusions. To build reliable relationships between face appearance and shape with large view variations, we propose to formulate face alignment as an l1-induced Stagewise Relational Dictionary (SRD) learning problem. During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks. During testing, the SRD model automatically selects a sparse set of the most related shape displacements for the testing face and uses them to refine its shape iteratively. To locate facial landmarks under occlusions, we further propose to learn an occlusion dictionary to model different kinds of partial face occlusions. By deploying the occlusion dictionary into the SRD model, the alignment performance for occluded faces can be further improved. Our algorithm is simple, effective, and easy to implement. Extensive experiments on two benchmark datasets and two newly built datasets have demonstrated its superior performances over the state-of-the-art methods, especially for faces with large view variations and/or occlusions.
Local Outlier Detection Based on Kernel Regression

Outlier detection keeps an important and attractive task of the knowledge discovery in databases. In this paper, a novel approach named Multi-scale Local Kernel Regression is proposed. It transfers the unsupervised learning of outlier detection to the classic non-parameter regression learning. Through preprocessing the original data by the basic local density-based method, it adopts the local kernel regression estimator in the multiple scale neighborhoods to determine outliers. Experiments on several real life data sets demonstrate that this approach is promising in detection performance.
Level set tracking with dynamical shape priors

Dynamical shape priors are curical for level set-based non- rigid object tracking with noise, occlusions or background clutter. In this paper, we propose a level set tracking framework using dynamical shape priors to capture contours changes of an object in a periodic action sequence. The framework consists of two stages - off-line training and on-line tracking. During the off-line training stage, a graph- based dominant set clustering (DSC) method is applied to learn a shape codebook with each codeword representing a certain shape mode. Then a codeword transition matrix is learnt to characterize the temporal correlations of contours of an object. During the on-line tracking stage, we fuse the knowledge of shape priors and current observations, and adopt maximum a posteriori (MAP) estimation to predict the current shape mode. The experimental results on synthetic and real video sequences demonstrate the effectiveness of our method.
Diversity encouraging ensemble of convolutional networks for high performance action recognition

We present a simple and effective ensemble method, Diversity Encouraging Ensemble (DEE), for deep convolutional networks to boost their performances. By training the convolutional network in two stages, we generate multiple component networks without adding any training cost. On the one hand, we modify the structure parameters of component networks in the training process to enlarge the diversities of the networks, which is found to be beneficial to improving the ensemble performance. On the other hand, we exploit monotonous decreasing learning rate schedule to accelerate the speed of deep network converging to different local minima, and we decrease the training time of integrating multiple networks to that of training a single network from traditional multi-step learning policy. We evaluate our ensemble method on two challenging action datasets, UCF-101 and HMDB-51, and obtain performance improvements from single deep network and other ensemble methods. Our results also outperform many state-of-the-art action recognition methods.
Evaluating combinational color constancy methods on real-world images

Light color estimation is crucial to the color constancy problem. Past decades have witnessed great progress in solving this problem. Contrary to traditional methods, many researchers propose a variety of combinational color constancy methods through applying different color constancy mathematical models on an image simultaneously and then give out a final estimation in diverse ways. Although many comprehensive evaluations or reviews about color constancy methods are available, few focus on combinational strategies. In this paper, we survey some prevailing combinational strategies systematically; divide them into three categories and compare them qualitatively on three real-world image data sets in terms of the angular error and the perceptual Euclidean distance. The experimental results show that combinational strategies with training procedure always produces better performance.
Drug-taking instruments recognition

In this paper we propose an algorithm for the recognition of three kinds of drug-taking instruments, including bongs, hookahs and spoons. A global feature - Pyramid of Histograms of Orientation Gradients (PHOG) - is used to represent images. PHOG is calculated by partitioning an image into increasingly fine sub-regions and concatenating the appropriately weighted histograms of orientation gradients of each sub-region at each level. Then, different classifiers can be employed to handle this recognition problem. In our experiments, Support Vector Machines (SVM) with five different kernels and Random Forest are evaluated for our application and SVM with χ2 kernel shows the best performance. We also compare our method with the standard Bag-of-Words (BOW) model using SIFT features. Experimental results demonstrate that in our application, directly using appropriate global feature (PHOG) is better than using local feature (SIFT) and BOW model in both performance and complexity.
Multi-Modal Multiple-Instance Learning with the application to the cannabis webpage recognition

With the development of the World Wide Web, there exists more and more illicit drug Webpages. Thus, how to screen cannabis Webpages on the internet is a quite important issue. Conventional methods that only use the keyword-based or image-based approaches are not sufficient. We propose a Multi-Modal Multiple-Instance Learning (MMMIL) approach combining both text and image information for cannabis webpage recognition. The main technical contributions of our work are two-fold. First, the text information associated with images is used to build a pre-classifier, which can pre-select pseudo positive training bags from new Webpages to update multi-modal classifier. This can be seen as a pseudo active learning process. Second, we design an efficient instance selection technique by utilizing text information to speed up the training process without compromising the performance. The experiments on a dataset containing over 40,000 images for more than 4,000 Webpages demonstrate the effectiveness and efficiency of the proposed approach.
Efficient Clustering Aggregation Based on Data Fragments

Clustering aggregation, known as clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a single better clustering. Existing clustering aggregation algorithms are applied directly to data points, in what is referred to as the point-based approach. The algorithms are inefficient if the number of data points is large. We define an efficient approach for clustering aggregation based on data fragments. In this fragment-based approach, a data fragment is any subset of the data that is not split by any of the clustering results. To establish the theoretical bases of the proposed approach, we prove that clustering aggregation can be performed directly on data fragments under two widely used goodness measures for clustering aggregation taken from the literature. Three new clustering aggregation algorithms are described. The experimental results obtained using several public data sets show that the new algorithms have lower computational complexity than three well-known existing point-based clustering aggregation algorithms (Agglomerative, Furthest, and LocalSearch); nevertheless, the new algorithms do not sacrifice the accuracy.
Multi-cue based multi-target tracking using online random forests

Discriminative tracking has become popular tracking methods due to their descriptive power for foreground/background separation. Among these methods, online random forest is recently proposed and received a large amount of research attention due to its advantages such as efficiency and robustness to noise, etc. However, the fact that only one kind of features is used limits the discriminative performance of this tracker. Additionally, the standard online forest tracker works only for a single target object. In this paper, we introduce a novel tracking method that integrates multiple cues capturing both geometric structures and edge-based shape information. Compared with the current online random forest based tracking algorithm, the proposed multi-cue tracker is more robust thanks to the complimentary information provided from these hybrid cues. Furthermore, the new tracker can track multiple targets as well as single target object. The effectiveness of the proposed tracker is validated using five public sequences.
An Efficient Approach to Web Near-Duplicate Image Detection

This paper presents an improved bag-of-words (BoW) framework for detecting near-duplicates of images on the Web and makes three main contributions. Firstly, based on the SIFT feature descriptors, Locality-constrained Linear Coding (LLC) with the spatial pyramid is introduced to encode features. Secondly, a weighted Chi-square distance metric is proposed to compare two histograms, with an inverted indexing scheme for fast similarity evaluation. Thirdly, a 6K dataset consisting of eight categories of objects, which can also be applicable to image retrieval and classification, is built and will be made available to the public in the future. We verify our technique on two benchmarks: our 6K dataset and the publicly available University of Kentucky Benchmark (UKB). The promising experimental results demonstrate the effectiveness and efficiency of our approach for Web Near-Duplicate Image Detection (Web-NDID), which outperforms several state-of-the-art methods.
Deep learning-based learning to rank with ties for image re-ranking

In existing learning to rank problems, the learned ranking function sorts objects according to their predicted scores. Therefore, a full-ordering object list is obtained even if two or more objects have almost identical degrees of relevance (or called objects with ties). For objects containing ties, a more reasonable ranking approach is to learn a ranking function which can judge both the preference and ties relationships among objects. In this paper, we propose a new pairwise ranking algorithm and apply it to image re-ranking. Specifically, we utilize deep learning to re-rank images based on a new loss function. The ties-relationship is considered in both training and testing process. As a result, the learned ranking function can be used to rank objects containing ties. The experimental results demonstrate the effectiveness of the proposed algorithm.
Multi-task Sparse Learning with Beta Process Prior for Action Recognition

In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible. Learning the sparse representation under each feature modality is considered as a single task in MTSL. Since the tasks are generated from multiple features associated with the same visual input, they are not independent but inter-related. We introduce a Beta process(BP) prior to the hierarchical MTSL model, which efficiently learns a compact dictionary and infers the sparse structure shared across all the tasks. The MTSL model enforces the robustness in coefficient estimation compared with performing each task independently. Besides, the sparseness is achieved via the Beta process formulation rather than the computationally expensive L1 norm penalty. In terms of non-informative gamma hyper-priors, the sparsity level is totally decided by the data. Finally, the learning problem is solved by Gibbs sampling inference which estimates the full posterior on the model parameters. Experimental results on the KTH and UCF sports datasets demonstrate the effectiveness of the proposed MTSL approach for action recognition.
Using Markov Random Field and subspaces to perform object tracking

This paper combines Markov Random Field and subspaces to perform object tracking. We first sample some particles using particle filter, and then divide each particle to patches. For each particle, we optimize each patch's position and use Markov Random Field to represent the structure of the patches, including each patch's own position and the relations between neighbor patches. We also evaluate each patch and the whole sub image according to their subspaces respectively. Experimental results demonstrated the efficiency of our method.
Context-Dependent Random Walk Graph Kernels and Tree Pattern Graph Matching Kernels With Applications to Action Recognition

Graphs are effective tools for modeling complex data. Setting out from two basic substructures, random walks and trees, we propose a new family of context-dependent random walk graph kernels and a new family of tree pattern graph matching kernels. In our context-dependent graph kernels, context information is incorporated into primary random walk groups. A multiple kernel learning algorithm with a proposed  $l_{1,2}$ -norm regularization is applied to combine context-dependent graph kernels of different orders. This improves the similarity measurement between graphs. In our tree-pattern graph matching kernel, a quadratic optimization with a sparse constraint is proposed to select the correctly matched tree-pattern groups. This augments the discriminative power of the tree-pattern graph matching. We apply the proposed kernels to human action recognition, where each action is represented by two graphs which record the spatiotemporal relations between local feature vectors. Experimental comparisons with state-of-the-art algorithms on several benchmark data sets demonstrate the effectiveness of the proposed kernels for recognizing human actions. It is shown that our kernel based on tree-pattern groups, which have more complex structures and exploit more local topologies of graphs than random walks, yields more accurate results but requires more runtime than the context-dependent walk graph kernel.
Joint Scale-Spatial Correlation Tracking with Adaptive Rotation Estimation

Boosted by large and standardized benchmark datasets, visual object tracking has made great progress in recent years and brought about many new trackers. Among these trackers, correlation filter based tracking schema exhibits impressive robustness and accuracy. In this work, we present a fully functional correlation filter based tracking algorithm which is able to simultaneously model target appearance changes from spatial displacements, scale variations, and rotation transformations. The proposed tracker first represents the exhaustive template searching in the joint scale and spatial space by a block-circulant matrix. Then, by transferring the target template from the Cartesian coordinate system to the Log-Polar coordinate system, the circulant structure is well preserved for the target even after whole orientation rotation. With these novel representation and transformation, object tracking is efficiently and effectively performed in the joint space with fast Fourier Transform. Experimental results on the VOT2015 benchmark dataset demonstrate its superior performance over state-of-the-art tracking algorithms.
Learning Human Actions by Combining Global Dynamics and Local Appearance

In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-Euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods.
Deep Constrained Siamese Hash Coding Network and Load-Balanced Locality-Sensitive Hashing for Near Duplicate Image Detection

We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process. The load-balanced LSH significantly reduces the query time. Based on the proposed load-balanced LSH, we design an effective and feasible algorithm for near duplicate image detection. Extensive experiments on three benchmark data sets demonstrate the effectiveness of our deep siamese hash encoding network and load-balanced LSH.
A boosted semi-supervised learning framework for web page filtering

The World Wide Web provides great convenience for users to obtain information. However, there exists much harmful information on the internet, such as pornographic content and prohibited drugs' information. Thus, how to filter harmful web pages on the internet is quite an important issue. In general, the problem of harmful web page filtering is converted to that of web page classification, which needs plenty of well labeled training samples. However, the cost of labeling a large set of web pages is very expensive. To address this problem, we adopt a semi-supervised framework for web page filtering. In this framework, each web page is represented by bags of different features, extracted using its HTML structure. Then a semi-supervised learning strategy is taken for efficiently obtaining well labeled training samples. Finally, a boosting classifier is utilized for harmful web page filtering. Experiments have demonstrated the effectiveness of our framework.
Illumination Estimation Based on Bilayer Sparse Coding

Computational color constancy is a very important topic in computer vision and has attracted many researchers' attention. Recently, lots of research has shown the effects of using high level visual content cues for improving illumination estimation. However, nearly all the existing methods are essentially combinational strategies in which image's content analysis is only used to guide the combination or selection from a variety of individual illumination estimation methods. In this paper, we propose a novel bilayer sparse coding model for illumination estimation that considers image similarity in terms of both low level color distribution and high level image scene content simultaneously. For the purpose, the image's scene content information is integrated with its color distribution to obtain optimal illumination estimation model. The experimental results on real-world image sets show that our algorithm is superior to some prevailing illumination estimation methods, even better than some combinational methods.
Multiple sample group pairs' graph embedding for tracking

This paper presents a new method which uses graph embedding and foreground-background patch pairs to perform object tracking. We first use particle filter to sample some particles. Then we evaluate each particle based on graph embedding and foreground-background patch pairs. For each particle, we use a two-layer model to represent the object, i.e. the inner layer (object layer) and the outer layer (background layer). Both the two layers are divided into patches. We cluster the foreground patches to several classes. Each class forms one sample group pair with the background patches. We perform graph embedding on multiple sample group pairs to discriminate the foreground and the background. Experimental results showed that our method tracked the objects efficiently.
Semi-Supervised Tensor-Based Graph Embedding Learning and Its Application to Visual Discriminant Tracking

An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a two-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learning- based semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object's appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.