Visual tracking based on transfer learning of deep salience information

In this paper,we propose a new visual tracking method in light of salience information and deep learning.Salience detection is used to exploit features with salient information of the image.Complicated representations of image features can be gained by the function of every layer in convolution neural network(CNN).The characteristic of biology vision in attention-based salience is similar to the neuroscience features of convolution neural network.This motivates us to improve the representation ability of CNN with functions of salience detection.We adopt the fully-convolution networks(FCNs)to perform salience detection.We take parts of the network structure to perform salience extraction,which promotes the classification ability of the model.The network we propose shows great performance in tracking with the salient information.Compared with other excellent algorithms,our algorithm can track the target better in the open tracking datasets.We realize the 0.5592 accuracy on visual object tracking 2015(VOT15)dataset.For unmanned aerial vehicle 123(UAV123)dataset,the precision and success rate of our tracker is 0.710 and 0.429.

The“Psychology”of Polygraph’:Engendering Differential Salience-Concerns and Caveats

The“success”of a polygraph examination is predicated on the establishment of differential or emotional salience(a“psychological set”)with an examinee.This,according to polygraph proponents,guarantees that an examinee will respond appropriately during the administration of the in-test(questioning)phase of the polygraph examination.However,polygraph procedure,as prescribed by its governing body,the American Polygraph Association(APA),is a static clinical Westernised process that does not make any provision for human multiplicity(culture/ethnicity,idiosyncrasies,level of education,language proficiency,ideologies,and so forth).Identical(one size fits all)test procedures are applied across the board–a highly controversial methodology.This article,instead of rigidly focusing on validity and reliability issues per se,explores the degree to which certain intentional and unintentional human behaviour modification strategies have the potential to counterbalance claimed polygraph rectitude from a metaphysical and discursive standpoint.The article exposes concerns(potential flaws)relating to polygraph theory in the context of the“psychological set”and is intended to serve as a caveat regarding the unmitigated use thereof.

AB064. Product knowledge predicts greater willingness to buy and gaze-related attention, salience does not

Background:Visual salience computed using algorithmic procedures have been shown to predict eye-movements in a number of contexts.However,despite calls to incorporate computationally-defined visual salience metrics as a means of assessing the effectiveness of advertisements,few studies have incorporated these techniques in a marketing context.The present study sought to determine the impact of visual salience and knowledge of a brand on eye-movement patterns and buying preferences.Methods:Participants(N=38)were presented with 54 pairs of products presented on the left and right sides of a blank white screen.For each pair,one product was a known North American product,such as Fresca®,and one was an unknown British product of the same category,such as Irn Bru®.Participants were asked to select which product they would prefer to buy while their eye movements were recorded.Salience was computed using Itti&Koch’s[2001]computational model of bottom-up salience.Products were defined as highly salient if the majority of the first five predicted fixations were in the region of the product.Results:Results showed that participants were much more likely to prefer to buy known products,and tentative evidence suggests that participants had longer total dwell times when looking at unknown products.Salience appears to have had little or no effect on preference for a product,nor did it predict total dwell time or time to first fixation.There also appears to be no interaction between knowledge of a product and visual salience on any of the measures analyzed.Conclusions:The results indicate that product salience may not be a useful predictor of attention under the constraints of the present experiment.Future studies could use a different operational definition of visual salience which might be more predictive of visual attention.Furthermore,a more fine-grained analysis of product familiarity based on survey data may reveal patterns obscured by the definitional constraints of the present study.

Giora的Graded Salience Hypothesis译名探讨

Giora的Graded Salience Hypothesis是语言学研究领域中的重要理论,其中文译名五花八门,不统一。译名的混乱不利于学术交流与研究,因此规范Graded Salience Hypothesis的中文译名非常有必要且迫切。Graded Salience Hypothesis认为,对比喻义和字面义的理解由一种普遍的Salience原则支配,即首先要处理salient意义。根据Giora的理论,结合相关的翻译标准,在分析Graded Salience Hypothesis词义构成的基础上,本文讨论其多种译名,发现"等级突显假说"为最佳翻译。"等级突显假说"是一个新的解释话语理解中词义激活与处理过程的理论,它的运用范围非常广泛,常用于心理学、神经学、医学诊断、语言理解、语言习得、翻译等研究中。在语言学领域,特别是在反语、惯用语、跨文化语用研究中,该理论越来越受重视。

Data-driven approach to learning salience models of indoor landmarks by using genetic programming

In landmark-based way-finding,determining the most salient landmark from several candidates at decision points is challenging.To overcome this problem,current approaches usually rely on a linear model to measure the salience of landmarks.However,linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived salience.Furthermore,the numbers of evaluated scenes and of volunteers participating in the testing of these models are often limited.With the aim of overcoming these gaps,we propose learning a non-linear salience model by means of genetic programming.We compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping malls.Two hundred volunteers who were not in these environments were asked to answer questionnaires about the collected photographs.The results from this experiment showed that in 76%of the cases,the most salient landmark(according to the volunteers’perception)was correctly predicted by our proposed approach.This accuracy rate is considerably higher than the ones achieved by conventional linear models.

Enhanced Object Detection and Classification via Multi-Method Fusion

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

Visual salience guided feature-aware shape simplification

In the area of 3D digital engineering and 3D digital geometry processing, shape simplification is an important task to reduce their requirement of large memory and high time complexity. By incorporating the content-aware visual salience measure of a polygonal mesh into simplification operation, a novel feature-aware shape simplification approach is presented in this paper. Owing to the robust extraction of relief heights on 3D highly detailed meshes, our visual salience measure is defined by a center-surround operator on Gaussian-weighted relief heights in a scale-dependent manner. Guided by our visual salience map, the feature-aware shape simplification algorithm can be performed by weighting the high-dimensional feature space quadric error metric of vertex pair contractions with the weight map derived from our visual salience map. The weighted quadric error metric is calculated in a six-dimensional feature space by combining the position and normal information of mesh vertices. Experimental results demonstrate that our visual salience guided shape simplification scheme can adaptively and effectively re-sample the underlying models in a feature-aware manner, which can account for the visually salient features of the complex shapes and thus yield better visual fidelity.

Stasis Salience and the Enthymemic Thesis

The argumentative stasis theory and enthymeme principles richly complement each other but they have rarely been investigated jointly. We correct this oversight first with a principled re-analysis of the stasis tradition, resulting in a double-layer stasis system: Cicero's later system(in De Oratore and Topica) with "action" stasis' subclassification, modified by Kenneth Burke's dramatic pentad of act, scene, agent, agency, purpose(in A Grammar of Motives). Then inspired by Ronald Langacker's salience theory in cognitive linguistics, we secure two stasis deployment strategies: selection(profile against base) and prominence(trajector against landmark). Stasis theory thus solidified, we examine how it interacts with the two central aspects of the enthymemic thesis: incompleteness and probability and how the enthymemic thesis helps explain the force of stasis theory. This inquiry contributes to rhetorical theory and criticism; argumentation studies; and linguistics, by showing the reach of salience theory.

Refined Sparse Representation Based Similar Category Image Retrieval

Given one specific image,it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images.However,traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances,ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image.Aiming to solve this problem above,we proposed in this paper one refined sparse representation based similar category image retrieval model.On the one hand,saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future.On the other hand,the cross mutual sparse coding model aims to extract the image’s essential feature to the maximumextent possible.At last,we set up a database concluding a large number of multi-source images.Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively.Moreover,adequate groups of ablation experiments show that nearly all procedures play their roles,respectively.

Interpreting Randomly Wired Graph Models for Chinese NER

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing(NLP)tasks.However,most existing approaches only focus on improving the performance of models but ignore their interpretability.In this work,we propose a Randomly Wired Graph Neural Network(RWGNN)by using graph to model the structure of Neural Network,which could solve two major problems(word-boundary ambiguity and polysemy)of ChineseNER.Besides,we develop a pipeline to explain the RWGNNby using Saliency Map and Adversarial Attacks.Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden states of RWGNN.

A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

This paper proposes a cascade deep convolutional neural network to address the loosening detection problem of bolts on axlebox covers.Firstly,an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers.And then,theA2-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts.Finally,a rectangular approximationmethod is proposed to regularize themarker line regions asaway tocalculate the angle of themarker line and plot all the angle values into an angle table,according to which the criteria of the angle table can determine whether the bolt with the marker line is in danger of loosening.Meanwhile,our improved algorithm is compared with the pre-improved algorithmin the object localization stage.The results show that our proposed method has a significant improvement in both detection accuracy and detection speed,where ourmAP(IoU=0.75)reaches 0.77 and fps reaches 16.6.And in the saliency detection stage,after qualitative comparison and quantitative comparison,our method significantly outperforms other state-of-the-art methods,where our MAE reaches 0.092,F-measure reaches 0.948 and AUC reaches 0.943.Ultimately,according to the angle table,out of 676 bolt samples,a total of 60 bolts are loose,69 bolts are at risk of loosening,and 547 bolts are tightened.

A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

Unconstrained face images are interfered by many factors such as illumination,posture,expression,occlusion,age,accessories and so on,resulting in the randomness of the noise pollution implied in the original samples.In order to improve the sample quality,a weighted block cooperative sparse representation algorithm is proposed based on visual saliency dictionary.First,the algorithm uses the biological visual attention mechanism to quickly and accurately obtain the face salient target and constructs the visual salient dictionary.Then,a block cooperation framework is presented to perform sparse coding for different local structures of human face,and the weighted regular term is introduced in the sparse representation process to enhance the identification of information hidden in the coding coefficients.Finally,by synthesising the sparse representation results of all visual salient block dictionaries,the global coding residual is obtained and the class label is given.The experimental results on four databases,that is,AR,extended Yale B,LFW and PubFig,indicate that the combination of visual saliency dictionary,block cooperative sparse representation and weighted constraint coding can effectively enhance the accuracy of sparse representation of the samples to be tested and improve the performance of unconstrained face recognition.

ELM-Based Shape Adaptive DCT Compression Technique for Underwater Image Compression

Underwater imagery and transmission possess numerous challenges like lower signal bandwidth,slower data transmission bit rates,Noise,underwater blue/green light haze etc.These factors distort the estimation of Region of Interest and are prime hurdles in deploying efficient compression techniques.Due to the presence of blue/green light in underwater imagery,shape adaptive or block-wise compression techniques faces failures as it becomes very difficult to estimate the compression levels/coefficients for a particular region.This method is proposed to efficiently deploy an Extreme Learning Machine(ELM)model-based shape adaptive Discrete Cosine Transformation(DCT)for underwater images.Underwater color image restoration techniques based on veiling light estimation and restoration of images followed by Saliency map estimation based on Gray Level Cooccurrence Matrix(GLCM)features are explained.An ELM network is modeled which takes two parameters,signal strength and saliency value of the region to be compressed and level of compression(DCT coefficients and compression steps)are predicted by ELM.This method ensures lesser errors in the Region of Interest and a better trade-off between available signal strength and compression level.

The exercise-cognition relationship:A virtuous circle

Exercise and health psychology have generated 2 sets of empirical studies guided by separate theory-driven axes.The first axis focuses on the causal relationship between chronic exercise and cognition and,more particularly,high-level cognitive functions such as executive functions(EFs).The second axis examines factors influencing the adherence process to physical activity(PA).Research conducted during the past decade shows that these 2 topics are closely linked,with EFs and effortful control playing a pivotal role in the bidirectional relationship linking PA and mental/brain health.The present article supports the idea that an individual engaged in the regular practice of effortful PA initiates a virtuous circle linking PA and effortful control in a bidirectional way.On the one hand,chronic exercise leads to an improvement of EFs and effortful control.On the other hand,gains in EFs and effortful control effectiveness lead to a reciprocal facilitation of the maintenance of PA over time.Some limitations and perspectives to this effort hypothesis are proposed in the last part of the article.

Validity,reliability,and psychometric properties of a computerized,cognitive assessment test(Cognivue^®)Validity,reliability,and psychometric properties of a computerized,cognitive assessment test(Cognivue^®)

BACKGROUND Cognitive issues such as Alzheimer’s disease and other dementias confer a substantial negative impact.Problems relating to sensitivity,subjectivity,and inherent bias can limit the usefulness of many traditional methods of assessing cognitive impairment.AIM To determine cut-off scores for classification of cognitive impairment,and assess Cognivue®safety and efficacy in a large validation study.METHODS Adults(age 55-95 years)at risk for age-related cognitive decline or dementia were invited via posters and email to participate in two cohort studies conducted at various outpatient clinics and assisted-and independent-living facilities.In the cut-off score determination study(n=92),optimization analyses by positive percent agreement(PPA)and negative percent agreement(NPA),and by accuracy and error bias were conducted.In the clinical validation study(n=401),regression,rank linear regression,and factor analyses were conducted.Participants in the clinical validation study also completed other neuropsychological tests.RESULTS For the cut-off score determination study,92 participants completed St.Louis University Mental Status(SLUMS,reference standard)and Cognivue^®tests.Analyses showed that SLUMS cut-off scores of<21(impairment)and>26(no impairment)corresponded to Cognivue^®scores of 54.5(NPA=0.92;PPA=0.64)and 78.5(NPA=0.5;PPA=0.79),respectively.Therefore,conservatively,Cognivue^®scores of 55-64 corresponded to impairment,and 74-79 to no impairment.For the clinical validation study,401 participants completed≥1 testing session,and 358 completed 2 sessions 1-2 wk apart.Cognivue^®classification scores were validated,demonstrating good agreement with SLUMS scores(weightedκ0.57;95%CI:0.50-0.63).Reliability analyses showed similar scores across repeated testing for Cognivue^®(R2=0.81;r=0.90)and SLUMS(R2=0.67;r=0.82).Psychometric validity of Cognivue^®was demonstrated vs.traditional neuropsychological tests.Scores were most closely correlated with measures of verbal processing,manual dexterity/speed,visual contrast sensitivity,visuospatial/executive function,and speed/sequencing.CONCLUSION Cognivue^®scores≤50 avoid misclassification of impairment,and scores≥75 avoid misclassification of unimpairment.The validation study demonstrates good agreement between Cognivue^®and SLUMS;superior reliability;and good psychometric validity.

Differential aberrant connectivity of precuneus and anterior insula may underpin the diagnosis of schizophrenia and mood disorders

BACKGROUND Over the past decade,resting-state functional magnetic resonance imaging(rsfMRI)has concentrated on brain networks such as the default mode network(DMN),the salience network(SN),and the central executive network(CEN),allowing for a better understanding of cognitive deficits observed in mental disorders,as well as other characteristic psychopathological phenomena such as thought and behavior disorganization.AIM To investigate differential patterns of effective connectivity across distributed brain networks involved in schizophrenia(SCH)and mood disorders.METHODS The sample comprised 58 patients with either paranoid syndrome in the context of SCH(n=26)or depressive syndrome(Ds)(n=32),in the context of major depressive disorder or bipolar disorder.The methods used include rs-fMRI and subsequent dynamic causal modeling to determine the direction and strength of connections to and from various nodes in the DMN,SN and CEN.RESULTS A significant excitatory connection from the dorsal anterior cingulate cortex to the anterior insula(aI)was observed in the SCH patient group,whereas inhibitory connections from the precuneus to the ventrolateral prefrontal cortex and from the aI to the precuneus were observed in the Ds group.CONCLUSION The results delineate specific patterns associated with SCH and Ds and offer a better explanation of the underlying mechanisms of these disorders,and inform differential diagnosis and precise treatment targeting.

A network-based cognitive training induces cognitive improvements and neuroplastic changes in patients with relapsing-remitting multiple sclerosis:an exploratory case-control study

Cognitive impairments are commonly observed in patients with multiple sclerosis and are associated with lower levels of quality of life.No consensus has been reached on how to tackle effectively cognitive decline in this clinical population non-pharmacologically.This exploratory case-control study aims to investigate the effectiveness of a hypothesis-based cognitive training designed to target multiple domains by promoting the synchronous co-activation of different brain areas and thereby improve cognition and induce changes in functional connectivity in patients with relapsing-remitting multiple sclerosis.Forty-five patients(36 females and 9 males,mean age 44.62±8.80 years)with clinically stable relapsing-remitting multiple sclerosis were assigned to either a standard cognitive training or to control groups(sham training and nonactive control).The standard training included twenty sessions of computerized exercises involving various cognitive functions supported by distinct brain networks.The sham training was a modified version of the standard training that comprised the same exercises and number of sessions but with increased processing speed load.The non-active control group received no cognitive training.All patients underwent comprehensive neuropsychological and magnetic resonance imaging assessments at baseline and after 5 weeks.Cognitive and resting-state magnetic resonance imaging data were analyzed using repeated measures models.At reassessment,the standard training group showed significant cognitive improvements compared to both control groups in memory tasks not specifically targeted by the training:the Buschke Selective Reminding Test and the Semantic Fluency test.The standard training group showed reductions in functional connectivity of the salience network,in the anterior cingulate cortex,associated with improvements on the Buschke Selective Reminding Test.No changes were observed in the sham training group.These findings suggest that multi-domain training that stimulates multiple brain areas synchronously may improve cognition in people with relapsing-remitting multiple sclerosis if sufficient time to process training material is allowed.The associated reduction in functional connectivity of the salience network suggests that training-induced neuroplastic functional reorganization may be the mechanism supporting performance gains.This study was approved by the Regional Ethics Committee of Yorkshire and Humber(approval No.12/YH/0474)on November 20,2013.

The Dynamic Relationship of Brain Networks Across Time Windows During Product-Based Creative Thinking

Consensus of creativity research suggests that the measurement of both originality and valuableness is necessary when designing creativity tasks.However,few studies have emphasized valuableness when exploring underlying neural substrates of creative thinking.The present study employs product-based creativity tasks that measure both originality and valuableness in an exploration of the dynamic relationship between the default mode(DMN),executive control(ECN),and salience(SN)networks through time windows.This methodology highlights relevance,or valuableness,in creativity evaluation as opposed to divergent thinking tasks solely measuring originality.The researchers identified seven brain regions belonging to the ECN,DMN,and SN as regions of interest(ROIs),as well as four representative seeds to analyze functional connectivity in 25 college student participants.Results showed that all of the identified ROIs were involved during the creative task.The insula,precuneus,and ventrolateral prefrontal cortex(vlPFC)remained active across all stages of product-based creative thinking.Moreover,the connectivity analyses revealed varied interaction patterns of DMN,ECN,and SN at different thinking stages.The integrated findings of the whole brain,ROI,and connectivity analyses suggest a trend that the DMN and SN(which relate to bottom-up thinking)attenuate as time proceeds,whereas the vlPFC(which relates to top-down thinking)gets stronger at later stages;these findings reflect the nature of our creativity tasks and decision-making of valuableness in later stages.Based on brain region activation throughout execution of the task,we propose that product-based creative process may include three stages:exploration and association,incubation and insight,and finally,evaluation and decision making.This model provides a thinking frame for further research and classroom instruction.

A Functional Inhibitory Role of Habenular Glucagon-Like Peptide-1 (GLP-1) in Forebrain Reward Signaling

There is emerging evidence implicating glucagon-like peptide-1 (GLP-1) in reward, including palatable food reinforcement and alcohol-based reward circuitry. While recent findings suggest that mesolimbic structures, such as the ventral tegmental area (VTA) and the nucleus accumbens (NAc), are critical anatomical sites mediating the role of GLP-1’s inhibitory actions, the present study focused on the potential novel impact of GLP-1 within the habenula, a region of the forebrain expressing GLP-1 receptors. Given that the habenula has also been implicated in the neural control of reward and reinforcement, we hypothesized that this brain region, like the VTA and NAc, might mediate the anhedonic effects of GLP-1. Rats were stereotaxically implanted with guide cannula targeting the habenula and trained on a progressive ratio 3 (PR3) schedule of reinforcement. Separate rats were trained on an alcohol two-bottle choice paradigm with intermittent access. The GLP-1 agonist exendin-4 (Ex-4) was administered directly into the habenula to determine the effects on operant responding for palatable food as well as alcohol intake. Our results indicated that Ex-4 reliably suppressed PR3 responding and that this effect was dose-dependent. A similar suppressive effect on alcohol consumption was observed. These findings provide initial and compelling evidence that the habenula may mediate the inhibitory action of GLP-1 on reward, including operant and drug reward. Our findings further suggest that GLP-1 receptor mechanisms outside of the midbrain and ventral striatum are critically involved in brain reward neurotransmission.