nils-herrmann/clean_scirepeval huggingface dataset:
Zero-Shot Learning Using Synthesised Unseen Visual Data with Diffusion Regularisation

Sufficient training examples are the fundamental requirement for most of the learning tasks. However, collecting well-labelled training examples is costly. Inspired by Zero-shot Learning (ZSL) that can make use of visual attributes or natural language semantics as an intermediate level clue to associate low-level features with high-level classes, in a novel extension of this idea, we aim to synthesise training data for novel classes using only semantic attributes. Despite the simplicity of this idea, there are several challenges. First, how to prevent the synthesised data from over-fitting to training classes? Second, how to guarantee the synthesised data is discriminative for ZSL tasks? Third, we observe that only a few dimensions of the learnt features gain high variances whereas most of the remaining dimensions are not informative. Thus, the question is how to make the concentrated information  diffuse to most of the dimensions of synthesised data. To address the above issues, we propose a novel embedding algorithm named Unseen Visual Data Synthesis (UVDS) that projects semantic features to the high-dimensional visual feature space. Two main techniques are introduced in our proposed algorithm. (1) We introduce a latent embedding space which aims to reconcile the structural difference between the visual and semantic spaces, meanwhile preserve the local structure. (2) We propose a novel Diffusion Regularisation (DR) that explicitly forces the variances to diffuse over most dimensions of the synthesised data. By an orthogonal rotation (more precisely, an orthogonal transformation), DR can remove the redundant correlated attributes and further alleviate the over-fitting problem. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data for zero-shot learning. Extensive experimental results suggest that our proposed approach significantly outperforms the state-of-the-art methods.
Cascade Learning by Optimally Partitioning

Cascaded AdaBoost classifier is a well-known efficient object detection algorithm. The cascade structure has many parameters to be determined. Most of existing cascade learning algorithms are designed by assigning detection rate and false positive rate to each stage either dynamically or statically. Their objective functions are not directly related to minimum computation cost. These algorithms are not guaranteed to have optimal solution in the sense of minimizing computation cost. On the assumption that a strong classifier is given, in this paper, we propose an optimal cascade learning algorithm (iCascade) which iteratively partitions the strong classifiers into two parts until predefined number of stages are generated. iCascade searches the optimal partition point <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {r_{i}}$ </tex-math></inline-formula> of each stage by directly minimizing the computation cost of the cascade. Theorems are provided to guarantee the existence of the unique optimal solution. Theorems are also given for the proposed efficient algorithm of searching optimal parameters <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {r_{i}}$ </tex-math></inline-formula>. Once a new stage is added, the parameter <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {r_{i}}$ </tex-math></inline-formula> for each stage decreases gradually as iteration proceeds, which we call decreasing phenomenon. Moreover, with the goal of minimizing computation cost, we develop an effective algorithm for setting the optimal threshold of each stage. In addition, we prove in theory why more new weak classifiers in the current stage are required compared to that of the previous stage. Experimental results on face detection and pedestrian detection demonstrate the effectiveness and efficiency of the proposed algorithm.
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance
Local Regression and Global Information-Embedded Dimension Reduction.

A large family of algorithms for unsupervised dimension reduction is based on both the local and global structures of the data. A fundamental step in these methods is to model the local geometrical structure of the data. However, the previous methods mainly ignore two facts in this step: 1) the dimensionality of the data is usually far larger than the number of local data, which is a typical ill-posed problem and 2) the data might be polluted by noise. These facts normally may lead to an inaccurate learned local structure and may degrade the final performance. In this paper, we propose a novel unsupervised dimension reduction method with the ability to address these problems effectively while also preserving the global information of the input data. Specifically, we first denoise the local data by preserving their principal components and we then apply a regularization term to the local modeling function to solve the illposed problem. Then, we use a linear regression model to capture the local geometrical structure, which is demonstrated to be insensitive to the parameters. Finally, we propose two criteria to simultaneously model both the local and the global information. Theoretical analyses for the relations between the proposed methods and some classical dimension-reduction methods are presented. The experimental results from various databases demonstrate the effectiveness of our methods.
An Improved Nyquist–Shannon Irregular Sampling Theorem From Local Averages

The Nyquist–Shannon sampling theorem is on the reconstruction of a band-limited signal from its uniformly sampled samples. The higher the signal bandwidth gets, the more challenging the uniform sampling may become. To deal with this problem, signal reconstruction from local averages has been studied in the literature. In this paper, we obtain an improved Nyquist–Shannon sampling theorem from general local averages. In practice, the measurement apparatus gives a weighted average over an asymmetrical interval. As a special case, for local averages from symmetrical interval, we show that the sampling rate is much lower than that of a result by Gröchenig. Moreover, we obtain two exact dual frames from local averages, one of which improves a result by Sun and Zhou. At the end of this paper, as an example application of local average sampling, we consider a reconstruction algorithm: the piecewise linear approximations.
Target-oriented shape modeling with structure constraint for image segmentation

Image segmentation plays a critical role in medical imaging applications, whereas it is still a challenging problem due to the complex shapes and complicated texture of structures in medical images. Model based methods have been widely used for medical image segmentation as a priori knowledge can be incorporated. Accurate shape prior estimation is one of the major factors affecting the accuracy of model based segmentation methods. This paper proposes a novel statistical shape modeling method, which aims to estimate target-oriented shape prior by applying the constraint from the intrinsic structure of the training shape set. The proposed shape modeling method is incorporated into a deformable model based framework for image segmentation. The experimental results showed that the proposed method can achieve more accurate segmentation compared with other existing methods.
Scalable Linear Visual Feature Learning via Online Parallel Nonnegative Matrix Factorization

Visual feature learning, which aims to construct an effective feature representation for visual data, has a wide range of applications in computer vision. It is often posed as a problem of nonnegative matrix factorization (NMF), which constructs a linear representation for the data. Although NMF is typically parallelized for efficiency, traditional parallelization methods suffer from either an expensive computation or a high runtime memory usage. To alleviate this problem, we propose a parallel NMF method called alternating least square block decomposition (ALSD), which efficiently solves a set of conditionally independent optimization subproblems based on a highly parallelized fine-grained grid-based blockwise matrix decomposition. By assigning each block optimization subproblem to an individual computing node, ALSD can be effectively implemented in a MapReduce-based Hadoop framework. In order to cope with dynamically varying visual data, we further present an incremental version of ALSD, which is able to incrementally update the NMF solution with a low computational cost. Experimental results demonstrate the efficiency and scalability of the proposed methods as well as their applications to image clustering and image retrieval.
A-Optimal Non-negative Projection for image representation

As a central problem in computer vision and pattern recognition, data representation has attracted great attention in the past years. Non-negative matrix factorization (NMF) which is a useful data representation method makes great contribution on finding the latent structure of the data and leads to a parts-based representation by decomposing the data matrix into a few bases and encodings with nonnegative constraints. However, non-negative constraint is insufficient for getting more robust data representation. In this paper, we propose a novel method, called A-Optimal Non-negative Projection (ANP) for image data representation and further analysis. ANP imposes a constraint on the encoding factor as a regularizer during matrix factorization. In this way, the learned data representation leads to a stable linear model no matter what kind of data label is selected for further processing. Thus, it can preserve more intrinsic characteristics of the data regardless of any specific labels. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.
Learning to detect stereo saliency

This paper develops a novel learning-based method for detecting stereo saliency in stereopair images. The disparity maps computed from stereopair images provide an additional depth cue for stereo saliency detection. To the best of our knowledge, our approach is the first one to simultaneously detect the stereo saliency of both left and right images using support vector machine (SVM). In our work, the disparity maps are used in two aspects. One is to improve the performance of saliency detection for monocular image. The other one is to maintain the consistency between the stereo matching and saliency maps. In order to meet the above requirements, we propose a new combinational saliency feature to train the stereo images with the labeled saliency ground truth, using support vector machine as the classifier. In the test stage, our approach generates the stereo saliency results according to the trained SVM model. Furthermore, a stereopair saliency dataset containing 400 pairs of images is created to perform the challenging experiments. The experimental results have demonstrated that our method achieves better performance than the state-of-the-art algorithms of single-image saliency detection.
Coupled Directional Level Set for MR Image Segmentation

Segmenting bladder wall for thickness measuring is a fundamental operation in bladder magnetic resonance (MR) image analysis since thickening of the bladder wall may indicate abnormality. Active contours have been used for bladder wall segmentation, which can be broadly divided into gradient-based and region-based methods, according to the used image features. However, the artifacts in MR images and the complex background outside the bladder lead to significant challenges for segmentation. In this paper, a coupled directional level set model is proposed to segment the outer and inner boundaries simultaneously by exploiting the directional gradient, region information and thickness prior of the bladder wall. With our proposed method, the influence of the artifacts in the bladder lumen and the complicated intensity distribution of soft tissues surrounding the bladder can be appreciably reduced. Promising results on 119 bladder MR images have demonstrated the performance of the presented method.
Efficient Outlier Detection for High-Dimensional Data

How to tackle high dimensionality of data effectively and efficiently is still a challenging issue in machine learning. Identifying anomalous objects from given data has a broad range of real-world applications. Although many classical outlier detection or ranking algorithms have been witnessed during the past years, the high-dimensional problem, as well as the size of neighborhood, in outlier detection have not yet attracted sufficient attention. The former may trigger the distance concentration problem that the distances of observations in high-dimensional space tend to be indiscernible, whereas the latter requires appropriate values for parameters, making models high complex and more sensitive. To partially circumvent these problems, especially the high dimensionality, we introduce a concept called local projection score (LPS) to represent deviation degree of an observation to its neighbors. The LPS is obtained from the neighborhood information by the technique of low-rank approximation. The observation with high LPS is a promising candidate of outlier in high probability. Based on this notion, we propose an efficient and effective outlier detection algorithm, which is also robust to the parameter  ${k}$  of  ${k}$  nearest neighbors. Extensive evaluation experiments conducted on twelve public real-world data sets with five popular outlier detection algorithms show that the performance of the proposed method is competitive and promising.
Pylon line spatial correlation assisted transmission line detection

A transmission line is one of the most hazardous objects to low altitude flying aircraft. Due to its extremely tiny size and unsalient visual features, transmission line detection (TLD) is a well-recognized problem. In this paper, a novel TLD method is proposed with the assistance of the spatial correlation between pylon and line for TLD. First, a unidirectional spatial mapping is built up to describe the pylon line spatial correlation. Then, the proposed pylon line spatial correlation and other line features are integrated into a Bayesian framework, which is trained in advance and used to estimate the probability of one line segment belonging to a transmission line. Compared with three other line-based TLD methods, the experimental results demonstrate that the proposed method can obtain better detection performance with higher detection rates and much lower false alarm rates.
Improving Level Set Method for Fast Auroral Oval Segmentation

Auroral oval segmentation from ultraviolet imager images is of significance in the field of spatial physics. Compared with various existing image segmentation methods, level set is a promising auroral oval segmentation method with satisfactory precision. However, the traditional level set methods are time consuming, which is not suitable for the processing of large aurora image database. For this purpose, an improving level set method is proposed for fast auroral oval segmentation. The proposed algorithm combines four strategies to solve the four problems leading to the high-time complexity. The first two strategies, including our shape knowledge-based initial evolving curve and neighbor embedded level set formulation, can not only accelerate the segmentation process but also improve the segmentation accuracy. And then, the latter two strategies, including the universal lattice Boltzmann method and sparse field method, can further reduce the time cost with an unlimited time step and narrow band computation. Experimental results illustrate that the proposed algorithm achieves satisfactory performance for auroral oval segmentation within a very short processing time.
Truncation Error Analysis on Reconstruction of Signal From Unsymmetrical Local Average Sampling

The classical Shannon sampling theorem is suitable for reconstructing a band-limited signal from its sampled values taken at regular instances with equal step by using the well-known sinc function. However, due to the inertia of the measurement apparatus, it is impossible to measure the value of a signal precisely at such discrete time. In practice, only unsymmetrically local averages of signal near the regular instances can be measured and used as the inputs for a signal reconstruction method. In addition, when implemented in hardware, the traditional sinc function cannot be directly used for signal reconstruction. We propose using the Taylor expansion of sinc function to reconstruct signal sampled from unsymmetrically local averages and give the upper bound of the reconstruction error (i.e., truncation error). The convergency of the reconstruction method is also presented.
A new sparse feature-based patch for dense correspondence

This paper presents a new method to compute the dense correspondences between two images by using the sparse feature-based patches in an energy optimization framework. Many transformation and deformation cues such as color, scale and rotation should be considered when we finding dense correspondences between images. However, most existing methods only consider part of these transformations, which will introduce the uncorrect correspondence results. In terms of the property of the sparse feature and the principle that nearest sub-scenes and neighbors are much more similar, we design a new energy optimization to guide the dense matching process. Both transformation and deformation are considered in our energy optimization framework since we design the feature-based patches. Thus, our algorithm can match the complicated scenes and objects robustly. At last, a local refinement technique is proposed to solve the perturbation of the matched patches. Experimental results demonstrate that our method outperforms the state-of-the-art algorithms.
Texture representation in AAM using Gabor wavelet and local binary patterns

Active appearance model (AAM) has been widely used for modeling the shape and the texture of deformable objects and matching new ones effectively. The traditional AAM consists of two parts, shape model and texture model. In the texture model, for the sake of simplicity, the image intensity is usually employed to represent the texture information. However, the intensity is easy to be interfered by the external environment change, e.g. illumination variations, which results in an unsatisfied model fitting. To this purpose, we present a new texture representation in AAM, which combines Gabor wavelet and Local Binary Patterns (LBP) operator. On the one hand, Gabor wavelet can encode multi-scale and multi-direction information of an image. On the other hand, LBP is able to efficiently encode local information and compress the redundancy in the Gabor filtered images. Since the new texture representation can express an object more sophisticatedly, it will improve the accuracy of the model fitting. The Experimental results on various datasets demonstrate the effectiveness of the proposed texture representation, which results in a more accurate and reliable matching between the model and new images.
Transfer Learning for Visual Categorization: A Survey

Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.
Practical analysis of watermarking capacity

Digital watermarking embedding is a hot research field of image processing. Digital watermarking is only possible because our vision system is not perfect. A number of applications have emerged such as copyright notification, time stamp and automated monitoring. There have been ingenious works on watermarking capacity, which all assume the attack distortion are Gaussian additive distribution. We argue that these results are beautiful but not practical. It is hard to classify different attacking methods into a uniform format (especially as Gaussian distribution). In the other hand, the capacity of a given image's information is an interesting and important topic. Instead of conventional analysis based on non-precise assumption of attack distortion, this paper concentrates on the problem of how much information that image can carry without much visibility distortion. Our work is based on wavelet transform and mean-squared error since we believe they are representative measure for most work. Comparing with the former work on the watermarking capacity, this paper pays more attention to the influence on capacity of widely used technique such as spread spectrum. In the last part of this paper, we discuss future trends of new watermarking algorithm and their influence on capacity.