nils-herrmann/clean_scirepeval huggingface dataset:
Learning deep compact descriptor with bagging auto-encoders for object retrieval

Content based object retrieval across large scale surveillance video dataset is a significant and challenging task, in which learning an effective compact object descriptor plays a critical role. In this paper, we propose an efficient deep compact descriptor with bagging auto-encoders. Specifically, we take advantage of discriminative CNN to extract efficient deep features, which not only involve rich semantic information but also can filter background noise. Besides, to boost the retrieval speed, auto-encoders are used to map the high-dimensional real-valued CNN features into short binary codes. Considering the instability of auto-encoder, we adopt a bagging strategy to fuse multiple auto-encoders to reduce the generalization error, thus further improving the retrieval accuracy. In addition, bagging is easy for parallel computing, so retrieval efficiency can be guaranteed. Retrieval experimental results on the dataset of 100k visual objects extracted from multi-camera surveillance videos demonstrate the effectiveness of the proposed deep compact descriptor.
A coarse-to-fine logo recognition method in video streams

Visual logo recognition is significant for many applications, such as enterprise identification, entertainment advertising, vehicle recognition, road sign reading, trademark protection, and much more. In this paper, we propose a coarse-to-fine framework to recognize visual logos from video streams. To reduce the instability of the initial template selection problem, we introduce the “iconic template” selection strategy to select effective template set for visual logos. At the coarse stage, we adopt DOT(Dominant Orientation Templates) matching with a low threshold to find logo candidates. At the fine stage, we transform the multiple template matching problem into a pairwise binary classification problem. The candidates collected from the template matching process combined with the target template are send to a pairwise binary classifier to predict whether the candidate and the template belong to the same logo or not. The pairwise binary classifier is trained in an offline manner and with an unsupervised training data collection strategy. The proposed method can flexibly adapt to different template matching approaches and various matching thresholds. The false-alarm rate is greatly reduced through the second stage. Experimental results show the feasibility and effectiveness of the proposed approach.
A Mid-Level Scene Change Representation Via Audiovisual Alignment

Scene is a series of semantic correlated video shots. An effective scene detection depends on domain knowledge more or less. Most existing approaches try to directly detect various scene changes by applying clustering or supervised learning methods to low level audiovisual features. However, robustly detecting diverse scene changes derived from complex semantic meanings is still a challenging problem. In this paper we are focused on the association of visual signal changes (e.g. cuts, fade-in, fade-out, etc.) and audio signal changes (e.g. speaker change, background music change, etc.) to propose a mid-level scene change representation, which is meant to locate candidate scene change points by characterizing temporally uncorrelated properties of audio and visual track in the case of scene change happening. By incorporating domain knowledge, enhanced features can be further extracted to complement this representation to bridge semantic gap towards scene change detection. We utilize a camera motion estimation algorithm to detect visual signal changes. Such visual change positions are selected as time-stamp points. An alignment is performed to search for candidate audio signal change positions by multi-scale Kullback-Leibler (K-L) distance computing. Both metric-based K-L distance approach and model-based HMM are applied to determine true audio signal changes. The associated visual and audio signal changes are considered as the mid-level scene change representation. This representation has been successfully applied to detect boundaries of individual commercial in TV broadcast stream with an accuracy of around 95%. Particularly the systematic alignment approach can be utilized in video summarization
CoupleNet: Coupling Global Structure with Local Parts for Object Detection

The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7% on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly available1.
Boosted local classifiers for visual tracking

Most existing discriminative tracking methods model a target object as a whole and train a tracker based on holistic templates, which cannot effectively deal with partial occlusions. Instead, in this paper, by treating the target as a collection of local patches, we propose a novel tracking approach based on boosted local classifiers. Initially, a set of local patches are sampled to train a set of local classifiers, and the weight of each classifier is given based on the estimated error. In addition, the positive examples and negative examples are sampled for model update with two constraints during the tracking process, which helps obtain more negatives for updating the appearance model and improve the updating efficiency. With updating the weights of local classifiers based on the temporal stability, the tracker can effectively handle partial occlusions. Extensive experiments on various challenging image sequences demonstrate the superiority to several state-of-the-art methods.
Robust Bayesian tracking on Riemannian manifolds via fragments-based representation

Recently, the covariance region descriptor  has been proved robust and versatile for a modest computational cost. It enables efficient fusion of different types of features. Based on the covariance descriptor and the metric on Riemannian manifolds, we develop a robust Bayesian tracking framework via fragments-based representation in this paper. In this framework, the template object is represented by multiple image fragments or patches. Every patch votes on the possible state of the object in the current frame, by comparing its covariance descriptor with the corresponding image patch model. Tracking is then led by the Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. The weight of each particle is formulated by combining the votes of the patches using a robust statistic. Further, we extend the fast covariance computation to the Bayesian tracking problem, which makes the tracking procedure more efficient. We present extensive experimental results on challenging sequences, which demonstrate the robust tracking achieved by our algorithm.
Multi-View 3D Object Retrieval With Deep Embedding Network

In multi-view 3D object retrieval, each object is characterized by a group of 2D images captured from different views. Rather than using hand-crafted features, in this paper, we take advantage of the strong discriminative power of convolutional neural network to learn an effective 3D object representation tailored for this retrieval task. Specifically, we propose a deep embedding network jointly supervised by classification loss and triplet loss to map the high-dimensional image space into a low-dimensional feature space, where the Euclidean distance of features directly corresponds to the semantic similarity of images. By effectively reducing the intra-class variations while increasing the inter-class ones of the input images, the network guarantees that similar images are closer than dissimilar ones in the learned feature space. Besides, we investigate the effectiveness of deep features extracted from different layers of the embedding network extensively and find that an efficient 3D object representation should be a tradeoff between global semantic information and discriminative local characteristics. Then, with the set of deep features extracted from different views, we can generate a comprehensive description for each 3D object and formulate the multi-view 3D object retrieval as a set-to-set matching problem. Extensive experiments on SHREC'15 data set demonstrate the superiority of our proposed method over the previous state-of-the-art approaches with over 12% performance improvement.
Semantic Linking between Video Ads and Web Services with Progressive Search

With the proliferation of online media services, ad video has become an important way to promote various products, services and ideas. Research efforts have been devoted to the contextual advertising whereas comprehensive recommendation of video ads is less exploited. In this paper, we propose to establish a semantic linking between video ads and relevant product/service online in a cross-media manner. First, we extract a representative key frame from the ad video and then conduct a three-step progressive search (i. e., visual search, tag aggregation and textual re-search) to link video ads with relevant Web service. We search visually similar product images, rank the context textual information by tags aggregation, and refine the results by textual re-search. Finally, we collect relevant products for user recommendation. Experiments on some popular E-commerce websites like eBay and Amazon have demonstrated the attractiveness of the semantic linking.
Research on Particle Swarm Optimization with Dynamic Inertia Weight

Particle Swarm Optimization (PSO) is a novel stochastic optimization algorithm based on the study of migration behaviors of bird flock in the process of searching food. Inertia weight, as an important parameter in PSO algorithm, plays a very important role in controlling the exploitation and exploration ability of algorithm. Recently, much more attention has been paid to the research of modified PSO based on dynamic inertia weight. This paper simply introduces the principle of PSO and overviews the research advances about dynamic inertia weight in existing references.
Joint background reconstruction and foreground segmentation via a two-stage convolutional neural network

Foreground segmentation in video sequences is a classic topic in computer vision. Due to the lack of semantic and prior knowledge, it is difficult for existing methods to deal with sophisticated scenes well. Therefore, in this paper, we propose an end-to-end two-stage deep convolutional neural network (CNN) framework for foreground segmentation in video sequences. In the first stage, a convolutional encoder-decoder sub-network is employed to reconstruct the background images and encode rich prior knowledge of background scenes. In the second stage, the reconstructed background and current frame are input into a multi-channel fully-convolutional sub-network (MCFCN) for accurate foreground segmentation. In the two-stage CNN, the reconstruction loss and segmentation loss are jointly optimized. The background images and foreground objects are output simultaneously in an end-to-end way. Moreover, by incorporating the prior semantic knowledge of foreground and background in the pre-training process, our method could restrain the background noise and keep the integrity of foreground objects at the same time. Experiments on CDNet 2014 show that our method outperforms the state-of-the-art by 4.9%.
Real-Time Probabilistic Covariance Tracking With Efficient Model Update

The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.
Simultaneous thermally stimulated current and thermoluminescence studies on polyethylene terephthalate film

We have measured simultaneously thermally stimulated current (TSC) and thermoluminescence (TL) of polyethylene terephthalate (PET) film. There are one broad peak in the temperature range of 150-250 K on both TL and TSC curves, the peak temperature (T/sub m/) on TL curve is about 194 K and the T/sub m/ on TSC one is about 208 K. In the temperature range of 255-320 K, TSC curve gives another broad peak. We analyze the curves by using our auto-resolving method and obtain a set of peaks. In the temperature range of 150-250 K, the activation energies of TL and TSC processes vary from 0.25 eV to 0.46 eV and 0.24 eV to 0.44 eV, respectively. For the other peak of TSC, the responding activation energies change from 0.45 eV to 0.53 eV. The initial carrier concentration in the traps both obey approximate Gauss distribution for the lower temperature peaks on TL and TSC curves, the peaks are ascribed to the motion of -COO group.
The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results

The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Link -- ping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.
Subspace learning based active learning for image retrieval

The goal of relevance feedback is to improve the performance of image retrieval by leveraging the labeling of human. It is helpful to introduce active learning method in relevance feedback to alleviate the human burden. In the traditional active learning the samples which can improve the classifier the most if they were labeled are selected for the user's labeling. However, the change of the geometrical structure of the data distribution caused by such expensive labeled samples is not fully exploited. By mining user's labeling information, we can reduce the original feature space dimension to ease the classifier's instability brought by the small sample size. In this paper, we propose a novel batch mode active learning method for informative data selection. The labeled samples are not only used to retrain the classifier, but to learn a subspace which efficiently encodes user's intention as well. Especially, a scheme of certainty propagation on the subspace effectively integrates uncertainty sampling and subspace learning into the proposed Subspace learning based batch mode Active Learning method (SubAL) in relevance feedback. Extensive experiments on publicly available dataset shows that the proposed method is promising.
A improved silhouette tracking approach integrating particle filter with graph cuts

In this paper, we propose a novel approach that combines particle filter tracking and 3D graph cut based segmentation to achieve silhouette tracking against drastic scale change and occlusion. The segmentation module offers particle filter tracking procedure the target shape information to compensate spatial information loss in the histogram based particle filter tracking process. Meanwhile, particle filter predicts a location for the shape prior in the segmentation module to overcome the global nature of graph cut algorithm, that is outlying regions similar with object are prone to be captured. The above two parts are linked with a guide mask that is initialized at the beginning of tracking and updated in a mask update mechanism. We demonstrate the effectiveness of our approach with experiments in several challenging image sequences.
Discriminative Context Models for Collective Activity Recognition

Context information has been widely studied for recognizing collective activities. Most existing works assume that all individuals in a single image share the same activity label. However, in many cases, multiple activities can be coexisted and serve as the context for each other in real-world scenarios. Based on this observation, we propose a novel approach to model both the intra-class and inter-class behavior interactions among persons in the scenario. By introducing the intra-class and inter-class context descriptors, we propose a unified discriminative model to jointly capture the individual appearance information and the context patterns around the focal person in a max-margin framework. Finally, a greedy forward search method is utilized to optimally label the activities in the testing scene. Experimental results demonstrate the superiority of our approach in activity recognition.
Target Tracking in Infrared Image Sequences Using Diverse AdaBoostSVM

This paper presents a novel algorithm named diverse AdaBoostSVM tracking (DABSVT) for target tracking in infrared imagery. The tracker trains a support vector machine (SVM) classifier per frame. All of the classifiers are combined into an ensemble classifier using AdaBoost. By proper parameter adjusting strategies, a set of effective SVM classifiers with moderate accuracy are obtained, and the dilemma problem between accuracy and diversity of AdaBoost is dealt with too. To cope with the changes in features of both foreground and background, the component classifier can be discarded or added at any time. The experiments performed on several sequences show the robustness of the proposed method
Multi-Information Fusing Based Railroad Object Detection

This paper proposes a railroad object detection algorithm based on multi information fusing, supplying the drawback of traditional surveillance methods in wild environment. Firstly, the railway is found in a given image by template matching. Then, integrity and consistency descriptors are computed to find out suspicious object. Finally, whether there exits an obstacle is judged in the railway via potential function after size revised, which is one of our main contributions. In addition, passing-by trains are detected via vibration signals. Our experiments in different kinds of wild railroad situations have showed better results than traditional methods.
Web Image Mining Based on Modeling Concept-Sensitive Salient Regions

In this paper, we propose a probabilistic model for Web image mining, which is based on concept-sensitive salient regions without human intervene. Our goal is to achieve a middle-level understanding of image semantics to bridge the semantic gap existing in the field of image mining and retrieval. With the help of a popular search engine, semantically relevant images are collected, and concept-sensitive salient regions are extracted automatically based on an attention model. Then the semantic concept model is learned from the joint distribution of all salient regions with Gaussian mixture model and expectation-maximization algorithm. In addition, by incorporating semantically irrelevant un-salient regions as negative samples, the discriminative power of the solution is further enhanced. Experiments demonstrate the encouraging performance of the proposed method
Anomaly detection in crowded scene via appearance and dynamics joint modeling

In this paper, we propose a novel solution of anomaly detection in crowd scene by jointly modeling appearance and dynamics of motion. First, a novel high-frequency feature based on optical flow (HFOF) is introduced. It can well capture the dynamic information of optical flow. Besides, we adopt the other two types of features, namely multi-scale histogram of optical(MHOF), and dynamic textures (DT). MHOF reserves the motion direction information, while DT captures appearance variant property. The three types of features can complement each other in modeling crowd motions. Finally, multiple kernel learning (MKL) is adopted to train a classifier for anomaly detection. Experiments are conducted on a publicly available dataset of escaping scenarios from University of Minnesota and a challenging dataset from Internet. The results of comparative experiments show the promising performance against other related work.
Dynamic simulation of a large scale 260t/h CFB boiler based on SIMUCAD platform

In the paper, an overall dynamic mathematical model of a 260t/h circulating fluidized bed (CFB) boiler was described, which included the sub-system mathematical models of the combustion chamber, the cyclone separator and the back-feeder in the 260t/h CFB boiler. Based on the mathematical models, using the SIMUCAD platform, a simulation environment of the boiler was developed, which can simulate the real 260t/h CFB boiler and produce the real-time simulation data. On the simulation environment, the overall mathematical model was validated against field test data generated in a real 260 t/h commercial CFB boiler produced by the Dongfang boiler group. The results demonstrate that the simulation data including the bed temperature and other critical parameters agree well with the field test data.