nils-herrmann/clean_scirepeval huggingface dataset:
Listwise Learning to Rank by Exploring Structure of Objects

Listwise learning to rank (LTR) is aimed at constructing a ranking model from listwise training data to order objects. In most existing studies, each training instance consists of a set of objects described by preference features. In a preference feature space for the objects in training, the structure of the objects is associated with the absolute preference degrees for the objects. The degrees significantly influence the ordering of the objects. Nevertheless, the structure of the training objects in their preference feature space has rarely been studied. In addition, most listwise LTR algorithms yield a single linear ranking model for all objects, but this ranking model may be insufficient to capture the underlying nonlinear ranking mechanism among all objects. This study proposes a divide-and-train method to learn a nonlinear ranking model from listwise training data. First, a rank-preserving clustering approach is used to infer the structure of objects in their preference feature space and all the objects in training data are divided into several clusters. Each cluster is assumed to correspond to a preference degree and an ordinal regression function is then learned. Second, considering that relations exist among the clusters, a multi-task listwise ranking approach is then employed to train linear ranking functions for all the clusters (or preference degrees) simultaneously. Our proposed method utilizes both the (relative) preferences among objects and the intrinsic structure of objects. Experimental results on benchmark data sets suggest that the proposed method outperforms state-oft-the-art listwise LTR algorithms.
Context-Aware Hypergraph Construction for Robust Spectral Clustering

Spectral clustering is a powerful tool for unsupervised data analysis. In this paper, we propose a context-aware hypergraph similarity measure (CAHSM), which leads to robust spectral clustering in the case of noisy data. We construct three types of hypergraphs-the pairwise hypergraph, the k-nearest-neighbor (kNN) hypergraph, and the high-order over-clustering hypergraph. The pairwise hypergraph captures the pairwise similarity of data points; the kNNhypergraph captures the neighborhood of each point; and the clustering hypergraph encodes high-order contexts within the dataset. By combining the affinity information from these three hypergraphs, the CAHSM algorithm is able to explore the intrinsic topological information of the dataset. Therefore, data clustering using CAHSM tends to be more robust. Considering the intra-cluster compactness and the inter-cluster separability of vertices, we further design a discriminative hypergraph partitioning criterion (DHPC). Using both CAHSM and DHPC, a robust spectral clustering algorithm is developed. Theoretical analysis and experimental evaluation demonstrate the effectiveness and robustness of the proposed algorithm.
Prototype Learning Using Metric Learning Based Behavior Recognition

Behavior recognition is an attractive direction in the computer vision domain. In this paper, we propose a novel behavior recognition method based on prototype learning using metric learning. Prototype learning algorithm can improve the classification performance of nearest-neighbor classifier, reduce the storage and computation requirements. And the metric learning algorithm is used to advance the performance of the prototype learning. In this paper, We use a kind of compound feature including local feature and motion feature to recognize human behaviors. The experimental results show the effectiveness of our method.
SCNN: Sequential convolutional neural network for human action recognition in videos

Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are two typical kinds of neural networks. While CNN models have achieved great success on image recognition due to their strong abilities in abstracting spatial information from multiple levels, RNN models have not achieved significant progress in video analyzing tasks (e.g. action recognition), although RNN can inherently model temporal dependencies from videos. In this work, we propose a Sequential Convolutional Neural Network, denoted as SCNN, to extract effective spatial-temporal features from videos, thus incorporating the strengths of both convolutional operation and recurrent operation. Our SCNN model extends RNN to directly process feature maps, rather than vectors flattened from feature maps, to keep spatial structures of the inputs. It replaces the full connections of RNN with convolutional connections to decrease parameter numbers, computational cost, and over-fitting risk. Moreover, we introduce asymmetric convolutional layers into SCNN to reduce parameter numbers and computational cost further. Our final SCNN deep architecture used for action recognition achieves very good performances on two challenging benchmarks, UCF-101 and HMDB-51, outperforming many state-of-the-art methods.
Semi-supervised Trajectory Learning Using a Multi-Scale Key Point Based Trajectory Representation

Motion trajectories contain rich high-level semantic information such as object behaviors and gestures, which can be effectively captured by supervised trajectory learning. However, it is usually a tough task to obtain a large number of high-quality manually labeled samples in real applications. Thus, how to perform trajectory learning in small training sample size situations is an important research topic. In this paper, we propose a trajectory learning framework using graph-based semi-supervised transductive learning, which propagates training sample labels along a particular graph. Furthermore, a novel trajectory descriptor based on multi-scale key points is proposed to characterize the spatial structural information. Experimental results demonstrate effectiveness of our framework.
Level set tracking with fusion of color and dynamical shape priors

Level set tracking has been widely used for tracking contours of objects. However, traditional methods are sensitive to noise, partial occlusions, background disturbance and some other factors, especially there exist serious problems for non-rigid objects tracking. With respect to this point, we propose a level set-based tracking framework in which color and dynamical shape priors are fused together. 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, firstly, the initial contour is evolved only with the color feature, a Markov random field (MRF)-like model measuring the correlations between neighboring pixels is added to improve the general region-based level set speed model; secondly, we fuse the knowledge of shape priors and the results obtained with the color feature, and adopt maximum a posteriori (MAP) estimation to predict the current shape mode; and finally the contours are evolved with the shape constraint continuously. The experimental results on synthetic and real video sequences demonstrate the effectiveness of our method.
Adaptive tracking with patches and a new particle filter

This paper presents a new tracking method based on color histogram model and patch appearance model. We propose a new particle filter which processes tracking concurrently in several routes, and each route creates particles separately. Each particle is optimized with a new regular iteration method. Then we evaluate each particle in two levels: the whole level and the patch level. The two levels use color histogram and patch images to evaluate the particles respectively. Different routes have different appearance models. We also maintain a combined appearance model. Experimental results demonstrated the effectiveness of our method.
Tensor Power Iteration for Multi-graph Matching

Due to its wide range of applications, matching between two graphs has been extensively studied and remains an active topic. By contrast, it is still under-exploited on how to jointly match multiple graphs, partly due to its intrinsic combinatorial intractability. In this work, we address this challenging problem in a principled way under the rank-1 tensor approximation framework. In particular, we formulate multi-graph matching as a combinational optimization problem with two main ingredients: unary matching over graph vertices and structure matching over graph edges, both of which across multiple graphs. Then we propose an efficient power iteration solution for the resulting NP-hard optimization problem. The proposed algorithm has several advantages: (1) the intrinsic matching consistency across multiple graphs based on the high-order tensor optimization, (2) the free employment of powerful high-order node affinity, (3) the flexible integration between various types of node affinities and edge/hyper-edge affinities. Experiments on diverse and challenging datasets validate the effectiveness of the proposed approach in comparison with state-of the-arts.
Multiclass spectral clustering based on discriminant analysis

Many existing spectral clustering algorithms share a conventional graph partitioning criterion: normalized cuts (NC). However, one problem with NC is that it poorly captures the graph¿s local marginal information which is very important to graph-based clustering. In this paper, we present a discriminant analysis based graph partitioning criterion (DAC), which is designed to effectively capture the graph¿s local marginal information characterized by the intra-class compactness and the inter-class separability. DAC preserves the intrinsic topological structures of the similarity graph on data points by constructing a k-nearest neighboring subgraph for each data point. Consequently, the clustering results generated by the DAC-based clustering algorithm (DACA) are robust to the outlier disturbance. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of DACA.
A New Approach for Video Scene Boundary Detection

Scene is the semantic unit in video. Video scene segmentation is a difficult task in content based video structure analysis. This paper proposes a new approach for scene boundary detection. We first construct shot content coherence signal using Normalized Cut criterion and then use a heuristic algorithm to detect scene boundary. Because the Normalized Cut criterion simultaneously emphasizes on the inhomogeneity of shots in different scenes and the homogeneity of shots in the same scene, the continuous signal reflects the coherence of shot content well. Experiments on different kinds of video clips demonstrate our approach performs well in scene segmentation.
Topic Detection for Discussion Threads with Domain Knowledge

The online communities are becoming so popular along with the development of the web but indexing and searching for the discussion data are big challenges to current applications. Topic detection was proposed to solve the problem but the accuracy is still not satisfactory, mainly because key elements are usually implicit or ambiguous which literal content comparison cannot handle. In this paper, we propose to improve the basic topic detection model by combining domain knowledge. The domain knowledge can be automatically extracted from a collection of external knowledge sources and applied to the content analysis of the threads. Two approaches, i.e. the LDA and the Concept Mapping, are proposed to implement the knowledge extraction and integration. Experimental results show that both approaches make the detection accuracy outperform the previous model. The LDA approach achieves better overall performance while the Concept Mapping is more suitable for dynamic knowledge sources.
Use bin-ratio information for category and scene classification

In this paper we propose using bin-ratio information, which is collected from the ratios between bin values of histograms, for scene and category classification. To use such information, a new histogram dissimilarity, bin-ratio dissimilarity (BRD), is designed. We show that BRD provides several attractive advantages for category and scene classification tasks: First, BRD is robust to cluttering, partial occlusion and histogram normalization; Second, BRD captures rich co-occurrence information while enjoying a linear computational complexity; Third, BRD can be easily combined with other dissimilarity measures, such as L1 and χ2, to gather complimentary information. We apply the proposed methods to category and scene classification tasks in the bag-of-words framework. The experiments are conducted on several widely tested datasets including PASCAL 2005, PASCAL 2008, Oxford flowers, and Scene-15 dataset. In all experiments, the proposed methods demonstrate excellent performance in comparison with previously reported solutions.
A Hierarchical Model Based on Latent Dirichlet Allocation for Action Recognition

Inspired by the recent success of hierarchical representation, we propose a new hierarchical variant of latent Dirichlet allocation (h-LDA) for action recognition. The model consists of an appearance group and a motion group, and we introduce a new hierarchical structure including two-layer topics in each group to learn the spatial temporal patterns (STPs) of human actions. The basic idea is that the two-layer topics are used to model the global STPs and the local STPs of the actions respectively. Two groups of discrete words are generated from two complementary kinds of features for each group. Each topic learned in these two groups is used to describe a particular aspect of the actions. Specifically, the mid-level topics are learned to describe the local STPs by including the geometric structure information in the lower-level words. The top-level topics are learned from the mid-level topics and are the mixture distribution of the local STPs, which makes the top-level topics appropriate to represent the global STPs. In addition, we give the learning and inference process by Gibbs sampling with reasonable assumptions. Finally, each sample is discriminatively represented as the probabilistic distribution over the global STPs learned by the proposed h-LDA. Experimental results on two datasets demonstrate the effectiveness of our approach for action recognition.
Discovering and Describing Activities by Trajectory Analysis

We propose a novel framework which automatically discovers and semantically describes typical activity patterns in surveillance situations, by analyzing object trajectories. In our framework, an activity pattern contains three elements: a) the source, b) the sink and c) the motion pattern. The sources and sinks in the scene are learned by clustering endpoints (origin and destination) of trajectories. The motion patterns in our framework are only related to temporal dynamics and invariant for spatial translation. They are learned through a 2-level hierarchical model. In the first level, the atomic motion patterns are learned by trajectory segmentation and sub-trajectory clustering. In the second level, the motion patterns are learned by clustering the sequences of atomic motion patterns. Combining the learned sources, sinks and motion patterns, activity patterns are easily distinguished and described in natural language. The effectiveness of our method is demonstrated by experiments on vehicle trajectories extracted in traffic scenes.
3D R Transform on Spatio-temporal Interest Points for Action Recognition

Spatio-temporal interest points serve as an elementary building block in many modern action recognition algorithms, and most of them exploit the local spatio-temporal volume features using a Bag of Visual Words (BOVW) representation. Such representation, however, ignores potentially valuable information about the global spatio-temporal distribution of interest points. In this paper, we propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the R transform which is defined as an extended 3D discrete Radon transform, followed by applying a two-directional two-dimensional principal component analysis. Such R feature captures the geometrical information of the interest points and keeps invariant to geometry transformation and robust to noise. In addition, we propose a new fusion strategy to combine the R feature with the BOVW representation for further improving recognition accuracy. We utilize a context-aware fusion method to capture both the pairwise similarities and higher-order contextual interactions of the videos. Experimental results on several publicly available datasets demonstrate the effectiveness of the proposed approach for action recognition.
HIGCALS: a hierarchical graph-theoretic clustering active learning system

Active learning aims at automatically selecting highly informative unseen data for humans to label under the condition that only few labeled samples are ready for use. Most of previous researches in active learning take advantage of supervised learning. Due to semantic gap problem, these existing active learning algorithms are not very effective on lessening human labeling efforts, especially in multiclass applications. In this paper, we propose a novel active learning framework based on unsupervised learning, and implement a hierarchical graph-theoretic clustering active learning system (HIGCALS). HIGCALS outperforms the existing active learning systems in several aspects, such as flexibility in system architecture and simpleness in system upgrade. Experiments on KDDCUP99 data set has demonstrated that HIGCALS can effectively reduce the workload of manual labeling without losing much accuracy.
Interclass visual similarity based visual vocabulary learning

Visual vocabulary is now widely used in many video analysis tasks, such as event detection, video retrieval and video classification. In most approaches the vocabularies are solely based on statistics of visual features and generated by clustering. Little attention has been paid to the interclass similarity among different events or actions. In this paper, we present a novel approach to mine the interclass visual similarity statistically and then use it to supervise the generation of visual vocabulary. We construct a measurement of interclass similarity, embed the similarity to the Euclidean distance and use the refined distance to generate visual vocabulary iteratively. The experiments in Weizmann and KTH datasets show that our approach outperforms the traditional vocabulary based approach by about 5%.
Network-based intrusion detection using Adaboost algorithm

Intrusion detection on the Internet is a heated research field in computer science, where much work has been done during the past two decades. In this paper, we build a network-based intrusion detection system using Adaboost, a prevailing machine learning algorithm. The experiments demonstrate that our system can achieve an especially low false positive rate while keeping a preferable detection rate, and its computational complexity is extremely low, which is a very attractive property in practice.
Horror Image Recognition Based on Context-Aware Multi-Instance Learning

Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the fuzzy support vector machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on the tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large-scale image sets collected from the Internet.
Robust Visual Tracking Based on Incremental Tensor Subspace Learning

Most existing subspace analysis-based tracking algorithms utilize a flattened vector to represent a target, resulting in a high dimensional data learning problem. Recently, subspace analysis is incorporated into the multilinear framework which offline constructs a representation of image ensembles using high-order tensors. This reduces spatio-temporal redundancies substantially, whereas the computational and memory cost is high. In this paper, we present an effective online tensor subspace learning algorithm which models the appearance changes of a target by incrementally learning a low-order tensor eigenspace representation through adaptively updating the sample mean and eigenbasis. Tracking then is led by the state inference within the framework in which a particle filter is used for propagating sample distributions over the time. A novel likelihood function, based on the tensor reconstruction error norm, is developed to measure the similarity between the test image and the learned tensor subspace model during the tracking. Theoretic analysis and experimental evaluations against a state-of-the-art method demonstrate the promise and effectiveness of this algorithm.
Combining the spatial and temporal eigen-space for visual tracking

Visual tracking is an important research topic in computer vision community. Most subspace based tracking algorithms focus on the time correlation between the image observations of the object, but the spatial layout information of the object is ignored. This paper proposes a robust visual tracking algorithm which effectively combines the spatial and temporal eigen-space of the object. In order to captures the variations of object appearance, an incremental updating strategy is developed to update the eigen-space and mean of the object. Experimental results demonstrate that, compared with the state-of-the-art subspace based tracking algorithms, the proposed tracking algorithm is more robust and effective.
Robust object tracking with boosted discriminative model via graph embedding

Recently discriminative based appearance model has achieved great success in modeling the object. It considers the tracking as the binary classification problem to separate the object from the background. However it neglects the object appearance and the object properties, it will encounter problems when the object lacks the features to separate it from the background. In this paper, we compensate the discriminative model with the object appearance through a unified graph-based constraint embedding framework. Volterra kernels approximation and log-Riemannian transformation are introduced to transform the discriminative model and object subspace learning problem into a graph embedding problem. Through the unified graph-based constraint embedding framework, the discriminative model is enhanced with the help of the object appearance. The effectiveness of our proposed approach is demonstrated in various experiments and quantitative evaluation using several challenging sequences.