nils-herrmann/clean_scirepeval huggingface dataset:
Breaking the interactive bottleneck in multi-class classification with active selection and binary feedback

Multi-class classification schemes typically require human input in the form of precise category names or numbers for each example to be annotated – providing this can be impractical for the user when a large (and possibly unknown) number of categories are present. In this paper, we propose a multi-class active learning model that requires only binary (yes/no type) feedback from the user. For instance, given two images the user only has to say whether they belong to the same class or not. We first show the interactive benefits of such a scheme with user experiments. We then propose a Value of Information (VOI)-based active selection algorithm in the binary feedback model. The algorithm iteratively selects image pairs for annotation so as to maximize accuracy, while also minimizing user annotation effort. To our knowledge, this is the first multi-class active learning approach that requires only yes/no inputs. Experiments show that the proposed method can substantially minimize user supervision compared to the traditional training model, on problems with as many as 100 classes. We also demonstrate that the system is robust to real-world issues such as class population imbalance and labeling noise.
Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS

Tracking by detection based object tracking methods encounter numerous complications including object appearance changes, size and shape deformations, partial and full occlusions, which make online adaptation of classifiers and object models a substantial challenge. In this paper, we employ an object proposal network that generates a small yet refined set of bounding box candidates to mitigate the this object model refitting problem by concentrating on hard negatives when we update the classifier. This helps improving the discriminative power as hard negatives are likely to be due to background and other distractions. Another intuition is that, in each frame, applying the classifier only on the refined set of object-like candidates would be sufficient to eliminate most of the false positives. Incorporating an object proposal makes the tracker robust against shape deformations since they are handled naturally by the proposal stage. We demonstrate evaluations on the PETS 2016 dataset and compare with the state-of-theart trackers. Our method provides the superior results.
Scene-Adaptive Human Detection with Incremental Active Learning

In many computer vision tasks, scene changes hinder the generalization ability of trained classifiers. For instance, a human detector trained with one set of images is unlikely to perform well in different scene conditions. In this paper, we propose an incremental learning method for human detection that can take generic training data and build a new classifier adapted to the new deployment scene. Two operation modes are proposed: i) a completely autonomous mode wherein first few empty frames of video are used for adaptation, and ii) an active learning approach with user in the loop, for more challenging scenarios including situations where empty initialization frames may not exist. Results show the strength of the proposed methods for quick adaptation.
Super-Trajectory for Video Segmentation

We introduce a novel semi-supervised video segmentation approach based on an efficient video representation, called as “super-trajectory”. Each super-trajectory corresponds to a group of compact trajectories that exhibit consistent motion patterns, similar appearance and close spatiotemporal relationships. We generate trajectories using a probabilistic model, which handles occlusions and drifts in a robust and natural way. To reliably group trajectories, we adopt a modified version of the density peaks based clustering algorithm that allows capturing rich spatiotemporal relations among trajectories in the clustering process. The presented video representation is discriminative enough to accurately propagate the initial annotations in the first frame onto the remaining video frames. Extensive experimental analysis on challenging benchmarks demonstrate our method is capable of distinguishing the target objects from complex backgrounds and even reidentifying them after occlusions.
Integral histogram: a fast way to extract histograms in Cartesian spaces

We present a novel method, which we refer as an integral histogram, to compute the histograms of all possible target regions in a Cartesian data space. Our method has three distinct advantages: 1) It is computationally superior to the conventional approach. The integral histogram method makes it possible to employ even an exhaustive search process in real-time, which was impractical before. 2) It can be extended to higher data dimensions, uniform and nonuniform bin formations, and multiple target scales without sacrificing its computational advantages. 3) It enables the description of higher level histogram features. We exploit the spatial arrangement of data points, and recursively propagate an aggregated histogram by starting from the origin and traversing through the remaining points along either a scan-line or a wave-front. At each step, we update a single bin using the values of integral histogram at the previously visited neighboring data points. After the integral histogram is propagated, histogram of any target region can be computed easily by using simple arithmetic operations.
Counting people by clustering person detector outputs

We present a people counting system that estimates the number of people in a scene by employing a clustering scheme based on Dirichlet Process Mixture Models (DP-MMs) which takes outputs of a person detector system as input. For each frame, we run a person detector on the frame, take its output as a set of detection areas and define a set of features based on spatial, color and temporal information for each detection. Then using these features, we cluster the detections using DPMMs and Gibbs sampling while having no restriction on the number of clusters, thus can estimate an arbitrary number of people or groups of people. We finally define a measure to calculate the actual number of people within each cluster to infer the final estimation of the number of people in the scene.
Kernel integral images: A framework for fast non-uniform filtering

Integral images are commonly used in computer vision and computer graphics applications. Evaluation of box filters via integral images can be performed in constant time, regardless of the filter size. Although Heckbert (1986) extended the integral image approach for more complex filters, its usage has been very limited, in practice. In this paper, we present an extension to integral images that allows for application of a wide class of non-uniform filters. Our approach is superior to Heckbertpsilas in terms of precision requirements and suitability for parallelization. We explain the theoretical basis of the approach and instantiate two concrete examples: filtering with bilinear interpolation, and filtering with approximated Gaussian weighting. Our experiments show the significant speedups we achieve, and the higher accuracy of our approach compared to Heckbertpsilas.
Learning a perspective-embedded deconvolution network for crowd counting

We present a novel deep learning framework for crowd counting by learning a perspective-embedded deconvolution network. Perspective is an inherent property of most surveillance scenes. Unlike the traditional approaches that exploit the perspective as a separate normalization, we propose to fuse the perspective into a deconvolution network, aiming to obtain a robust, accurate and consistent crowd density map. Through layer-wise fusion, we merge perspective maps at different resolutions into the deconvolution network. With the injection of perspective, our network is driven to learn to combine the underlying scene geometric constraints adaptively, thus enabling an accurate interpretation from high-level feature maps to the pixel-wise crowd density map. In addition, our network allows generating density map for arbitrary-sized input in an end-to-end fashion. The proposed method achieves competitive result on the WorldExpo2010 crowd dataset.
Not All Negatives Are Equal: Learning to Track With Multiple Background Clusters

Conventional tracking-by-detection approaches for visual object tracking often assume that the task at hand is a binary foreground-versus-background classification problem, in which the background is a single, generic, and all-inclusive class. In contrast, here we argue that the background appearance, for the most part, possesses a more complicated structure that would benefit from further partitioning into multiple contextual clusters. Our observation is that, although the background class is contemplated to contain a vast intra-class variation, during the tracking process, only a small portion of this diversity is present at the current frame around the foreground object. This observation motivates us to build multiple fine-grained foreground-versus-contextual-cluster models that provide more discriminative classifications, and consequently more robust and accurate foreground object tracking. For each cluster, we employ a structured output support vector machine (SSVM), and in an online manner, we combine the responses of multiple classifiers. To this end, we apply a top-level SSVM that models the tracked foreground object. We show that our refined modeling of the background is better than naïvely growing the complexity of a single foreground–background classifier, i.e., increasing the number of support vectors that existing approaches rely on, which cause overfitting issues. Our extensive evaluations on large benchmark data sets demonstrate that our tracker consistently outperforms the current state-of-the-art while having comparable computational requirements.
Ordered Pooling of Optical Flow Sequences for Action Recognition

Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input sample. For this purpose, a summarization approach that represents a set of consecutive RGB frames by a single dynamic image to capture pixel dynamics is proposed recently. In this paper, we introduce a novel ordered representation of consecutive optical flow frames as an alternative and argue that this representation captures the action dynamics more efficiently than RGB frames. We provide intuitions on why such a representation is better for action recognition. We validate our claims on standard benchmark datasets and demonstrate that using summaries of flow images lead to significant improvements over RGB frames while achieving accuracy comparable to the stateof-the-art on UCF101 and HMDB datasets.
A Cascaded Convolutional Neural Network for Single Image Dehazing

Images captured under outdoor scenes usually suffer from low contrast and limited visibility due to suspended atmospheric particles, which directly affects the quality of photographs. Despite numerous image dehazing methods have been proposed, effective hazy image restoration remains a challenging problem. Existing learning-based methods usually predict the medium transmission by convolutional neural networks (CNNs), but ignore the key global atmospheric light. Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the medium transmission and global atmospheric light jointly by two task-driven subnetworks. Specifically, the medium transmission estimation subnetwork is inspired by the densely connected CNN while the global atmospheric light estimation subnetwork is a light-weight CNN. Besides, these two subnetworks are cascaded by sharing the common features. Finally, with the estimated model parameters, the haze-free image is obtained by the atmospheric scattering model inversion, which achieves more accurate and effective restoration performance. Qualitatively and quantitatively experimental results on the synthetic and real-world hazy images demonstrate that the proposed method effectively removes haze from such images, and outperforms several state-of-the-art dehazing methods.
Concentric ring signature descriptor for 3D objects

We present a 3D feature descriptor that represents local topologies within a set of folded concentric rings by distances from local points to a projection plane. This feature, called as Concentric Ring Signature (CORS), possesses similar computational advantages to point signatures yet provides more accurate matches. It produces more compact and discriminative descriptors than shape context. It robust to noise and occlusions. As opposed to spin images, CORS does not require the point normal estimations, therefore it is directly applicable to sparse point clouds where the point densities are insufficiently low. Under the same settings, we demonstrate that the discriminative power of CORS is superior to conventional approaches producing twice as good estimates with the percentage of correct match scores improving from 39% to 88%.
A Unified Spatiotemporal Prior based on Geodesic Distance for Video Object Segmentation

Video saliency, aiming for estimation of a single dominant object in a sequence, offers strong object-level cues for unsupervised video object segmentation. In this paper, we present a geodesic distance based technique that provides reliable and temporally consistent saliency measurement of superpixels as a prior for pixel-wise labeling. Using undirected intra-frame and inter-frame graphs constructed from spatiotemporal edges or appearance and motion, and a skeleton abstraction step to further enhance saliency estimates, our method formulates the pixel-wise segmentation task as an energy minimization problem on a function that consists of unary terms of global foreground and background models, dynamic location models, and pairwise terms of label smoothness potentials. We perform extensive quantitative and qualitative experiments on benchmark datasets. Our method achieves superior performance in comparison to the current state-of-the-art in terms of accuracy and speed.
Domain Adaptation by Mixture of Alignments of Second-or Higher-Order Scatter Tensors

In this paper, we propose an approach to the domain adaptation, dubbed Second-or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second-or higher-order scatter statistics between the source and target domains. The human ability to learn from few labeled samples is a recurring motivation in the literature for domain adaptation. Towards this end, we investigate the supervised target scenario for which few labeled target training samples per category exist. Specifically, we utilize two CNN streams: the source and target networks fused at the classifier level. Features from the fully connected layers fc7 of each network are used to compute second-or even higher-order scatter tensors, one per network stream per class. As the source and target distributions are somewhat different despite being related, we align the scatters of the two network streams of the same class (within-class scatters) to a desired degree with our bespoke loss while maintaining good separation of the between-class scatters. We train the entire network in end-to-end fashion. We provide evaluations on the standard Office benchmark (visual domains) and RGB-D combined with Caltech256 (depth-to-rgb transfer). We attain state-of-the-art results.
Shape-Regulated Particle Filtering for Tracking Non-Rigid Objects

This paper presents an active contour based algorithm for tracking non-rigid objects in heavily cluttered scenes. We decompose the non-rigid contour tracking problem into three subproblems: 2D motion estimation, deformation detection, and shape regulation. First, we employ a particle filter to estimate the affine transform parameters between successive frames. Second, by using a dynamic object model, we generate a probabilistic map of deformation to reshape its contour. Finally, we project the updated model onto a trained shape subspace to constrain deformations to be within possible object appearances. Our experiments show that the proposed algorithm significantly improves the performance of the tracker.
Robust Object Tracking by Nonlinear Learning

We propose a method that obtains a discriminative visual dictionary and a nonlinear classifier for visual tracking tasks in a sparse coding manner based on the globally linear approximation for a nonlinear learning theory. Traditional discriminative tracking methods based on sparse representation learn a dictionary in an unsupervised way and then train a classifier, which may not generate both descriptive and discriminative models for targets by treating dictionary learning and classifier learning separately. In contrast, the proposed tracking approach can construct a dictionary that fully reflects the intrinsic manifold structure of visual data and introduces more discriminative ability in a unified learning framework. Finally, an iterative optimization approach, which computes the optimal dictionary, the associated sparse coding, and a classifier, is introduced. Experiments on two benchmarks show that our tracker achieves a better performance compared with some popular tracking algorithms.
Lie-Struck: Affine Tracking on Lie Groups Using Structured SVM

This paper presents a novel and reliable tracking-by detection method for image regions that undergo affine transformations such as translation, rotation, scale, dilatation and shear deformations, which span the six degrees of freedom of motion. Our method takes advantage of the intrinsic Lie group structure of the 2D affine motion matrices and imposes this motion structure on a kernelized structured output SVM classifier that provides an appearance based prediction function to directly estimate the object transformation between frames using geodesic distances on manifolds unlike the existing methods proceeding by linearizing the motion. We demonstrate that these combined motion and appearance model structures greatly improve the tracking performance while an incorporated particle filter on the motion hypothesis space keeps the computational load feasible. Experimentally, we show that our algorithm is able to outperform state-of-the-art affine trackers in various scenarios.
Image set classification by symmetric positive semi-definite matrices

Representing images and videos by covariance descriptors and leveraging the inherent manifold structure of Symmetric Positive Definite (SPD) matrices leads to enhanced performances in various visual recognition tasks. However, when covariance descriptors are used to represent image sets, the result is often rank-deficient. Thus, most existing approaches adhere to blind perturbation with predefined regularizers just to be able to employ inference tools. To overcome this problem, we introduce novel similarity measures specifically designed for rank-deficient covariance descriptors, i.e., symmetric positive semi-definite matrices. In particular, we derive positive definite kernels that can be decomposed into the kernels on the cone of SPD matrices and kernels on the Grassmann manifolds. Our experiments evidence that, our method achieves superior results for image set classification on various recognition tasks including hand gesture classification, face recognition from video sequences, and dynamic scene categorization.
When VLAD Met Hilbert

In many challenging visual recognition tasks where training data is limited, Vectors of Locally Aggregated Descriptors (VLAD) have emerged as powerful image/video representations that compete with or outperform state-of the-art approaches. In this paper, we address two fundamental limitations of VLAD: its requirement for the local descriptors to have vector form and its restriction to linear classifiers due to its high-dimensionality. To this end, we introduce a kernelized version of VLAD. This not only lets us inherently exploit more sophisticated classification schemes, but also enables us to efficiently aggregate nonvector descriptors (e.g., manifold-valued data) in the VLAD framework. Furthermore, we propose an approximate formulation that allows us to accelerate the coding process while still benefiting from the properties of kernel VLAD. Our experiments demonstrate the effectiveness of our approach at handling manifold-valued data, such as covariance descriptors, on several classification tasks. Our results also evidence the benefits of our nonlinear VLAD descriptors against the linear ones in Euclidean space using several standard benchmark datasets.
Dynamic bandwidth allocation with optimal number of renegotiations in ATM networks

This paper introduces a scheme for online dynamic bandwidth allocation for variable bit rate (VBR) traffic over ATM networks. The presented method determines the optimum bandwidth renegotiation time and bandwidth amount to allocate to a VBR traffic source by minimizing predefined cost functions. A traffic rate predictor designed by wavelets is provided as feedback to the system. The results show that the introduced scheme minimizes both under-utilization of the available capacity and queuing delays. The proposed method can also be deployed in weighted fair queuing disciplines to update dynamically a weight coefficient assigned to an application.
Boosting adaptive linear weak classifiers for online learning and tracking

Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak classifiers/features to adapt with the change over time. We are proposing a new online boosting algorithm where the form of the weak classifiers themselves are modified to cope with scene changes. Instead of replacement, the parameters of the weak classifiers are altered in accordance with the new data subset presented to the online boosting process at each time step. Thus we may avoid altogether the issue of how many weak classifiers to be replaced to capture the change in the data or which efficient search algorithm to use for a fast retrieval of weak classifiers. A computationally efficient method has been used in this paper for the adaptation of linear weak classifiers. The proposed algorithm has been implemented to be used both as an online learning and a tracking method. We show quantitative and qualitative results on both UCI datasets and several video sequences to demonstrate improved performance of our algorithm.