Long-term Tracking in the Wild: A Benchmark

We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused exclusively on sequences that are just tens of seconds in length and in which the target is always visible. Consequently, most researchers have designed methods tailored to this "short-term" scenario, which is poorly representative of practitioners' needs. Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object tracking datasets of recent years: it comprises 366 sequences spanning 14 hours of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used "in the wild". The project website is http://oxuva.netComment: To appear at ECCV 201

High Throughput Genetic Characterisation of Caucasian Patients Affected by Multi-Drug Resistant Rheumatoid or Psoriatic Arthritis

Rheumatoid and psoriatic arthritis (RA and PsA) are inflammatory rheumatic disorders characterised by a multifactorial etiology. To date, the genetic contributions to the disease onset, severity and drug response are not clearly defined, and despite the development of novel targeted therapies, ~10% of patients still display poor treatment responses. We characterised a selected cohort of eleven non-responder patients aiming to define the genetic contribution to drug resistance. An accurate clinical examination of the patients was coupled with several high-throughput genetic testing, including HLA typing, SNPs-array and Whole Exome Sequencing (WES). The analyses revealed that all the subjects carry very rare HLA phenotypes which contain HLA alleles associated with RA development (e.g., HLA-DRB1*04, DRB1*10:01 and DRB1*01). Additionally, six patients also carry PsA risk alleles (e.g., HLA-B*27:02 and B*38:01). WES analysis and SNPs-array revealed 23 damaging variants with 18 novel “drug-resistance” RA/PsA candidate genes. Eight patients carry likely pathogenic variants within common genes (CYP21A2, DVL1, PRKDC, ORAI1, UGT2B17, MSR1). Furthermore, “private” damaging variants were identified within 12 additional genes (WNT10A, ABCB7, SERPING1, GNRHR, NCAPD3, CLCF1, HACE1, NCAPD2, ESR1, SAMHD1, CYP27A1, CCDC88C). This multistep approach highlighted novel RA/PsA candidate genes and genotype-phenotype correlations potentially useful for clinicians in selecting the best therapeutic strategy

Unveiling the Power of Deep Tracking

In the field of generic object tracking numerous attempts have been made to exploit deep features. Despite all expectations, deep trackers are yet to reach an outstanding level of performance compared to methods solely based on handcrafted features. In this paper, we investigate this key issue and propose an approach to unlock the true potential of deep features for tracking. We systematically study the characteristics of both deep and shallow features, and their relation to tracking accuracy and robustness. We identify the limited data and low spatial resolution as the main challenges, and propose strategies to counter these issues when integrating deep features for tracking. Furthermore, we propose a novel adaptive fusion approach that leverages the complementary properties of deep and shallow features to improve both robustness and accuracy. Extensive experiments are performed on four challenging datasets. On VOT2017, our approach significantly outperforms the top performing tracker from the challenge with a relative gain of 17% in EAO

Siamese network based features fusion for adaptive visual tracking

© Springer Nature Switzerland AG 2018. Visual object tracking is a popular but challenging problem in computer vision. The main challenge is the lack of priori knowledge of the tracking target, which may be only supervised of a bounding box given in the first frame. Besides, the tracking suffers from many influences as scale variations, deformations, partial occlusions and motion blur, etc. To solve such a challenging problem, a suitable tracking framework is demanded to adopt different tracking scenes. This paper presents a novel approach for robust visual object tracking by multiple features fusion in the Siamese Network. Hand-crafted appearance features and CNN features are combined to mutually compensate for their shortages and enhance the advantages. The proposed network is processed as follows. Firstly, different features are extracted from the tracking frames. Secondly, the extracted features are employed via Correlation Filter respectively to learn corresponding templates, which are used to generate response maps respectively. And finally, the multiple response maps are fused to get a better response map, which can help to locate the target location more accurately. Comprehensive experiments are conducted on three benchmarks: Temple-Color, OTB50 and UAV123. Experimental results demonstrate that the proposed approach achieves state-of-the-art performance on these benchmarks

Routine Modeling with Time Series Metric Learning

version éditeur : https://rd.springer.com/chapter/10.1007/978-3-030-30484-3_47International audienceTraditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities. This work proposes to recognize recurrent activity patterns, called routines, instead of precisely defined activities. The modeling of routines is defined as a metric learning problem, and an architecture, called SS2S, based on sequence-to-sequence models is proposed to learn a distance between time series. This approach only relies on inertial data and is thus non intrusive and preserves privacy. Experimental results show that a clustering algorithm provided with the learned distance is able to recover daily routines

The role of left and right hemispheres in the comprehension of idiomatic language: an electrical neuroimaging study

<p>Abstract</p> <p>Background</p> <p>The specific role of the two cerebral hemispheres in processing idiomatic language is highly debated. While some studies show the involvement of the left inferior frontal gyrus (LIFG), other data support the crucial role of right-hemispheric regions, and particularly of the middle/superior temporal area. Time-course and neural bases of literal vs. idiomatic language processing were compared. Fifteen volunteers silently read 360 idiomatic and literal Italian sentences and decided whether they were semantically related or unrelated to a following target word, while their EEGs were recorded from 128 electrodes. Word length, abstractness and frequency of use, sentence comprehensibility, familiarity and cloze probability were matched across classes.</p> <p>Results</p> <p>Participants responded more quickly to literal than to idiomatic sentences, probably indicating a difference in task difficulty. Occipito/temporal N2 component had a greater amplitude in response to idioms between 250-300 ms. Related swLORETA source reconstruction revealed a difference in the activation of the left fusiform gyrus (FG, BA19) and medial frontal gyri for the contrast idiomatic-minus-literal. Centroparietal N400 was much larger to idiomatic than to literal phrases (360-550 ms). The intra-cortical generators of this effect included the left and right FG, the left cingulate gyrus, the right limbic area, the right MTG (BA21) and the left middle frontal gyrus (BA46). Finally, an anterior late positivity (600-800 ms) was larger to idiomatic than literal phrases. ERPs also showed a larger right centro-parietal N400 to associated than non-associated targets (not differing as a function of sentence type), and a greater right frontal P600 to idiomatic than literal associated targets.</p> <p>Conclusion</p> <p>The data indicate bilateral involvement of both hemispheres in idiom comprehension, including the right MTG after 350 ms and the right medial frontal gyrus in the time windows 270-300 and 500-780 ms. In addition, the activation of left and right limbic regions (400-450 ms) suggests that they have a role in the emotional connotation of colourful idiomatic language. The data support the view that there is direct access to the idiomatic meaning of figurative language, not dependent on the suppression of its literal meaning, for which the LIFG was previously thought to be responsible.</p

AI-powered transmitted light microscopy for functional analysis of live cells

Transmitted light microscopy can readily visualize the morphology of living cells. Here, we introduce artificial-intelligence-powered transmitted light microscopy (AIM) for subcellular structure identification and labeling-free functional analysis of live cells. AIM provides accurate images of subcellular organelles; allows identification of cellular and functional characteristics (cell type, viability, and maturation stage); and facilitates live cell tracking and multimodality analysis of immune cells in their native form without labeling

An outline of an asymmetric two-component theory of aspect

The paper presents the bases of an asymmetric two-component model of aspect. The main theoretical conclusion of the study is that (grammatical) viewpoint aspect and situation aspect are not independent aspectual levels, since the former often modifies the input situation aspect of the phrase/sentence. As it is shown, besides the arguments and adjuncts of the predicate, viewpoint aspect is also an important factor in compositionally marking situation aspect. The aspectual framework put forward in the paper is verified and illustrated on the basis of the aspectual system of Hungarian and some examples taken from English linguistic data

Processing of Hand-Related Verbs Specifically Affects the Planning and Execution of Arm Reaching Movements

Even though a growing body of research has shown that the processing of action language affects the planning and execution of motor acts, several aspects of this interaction are still hotly debated. The directionality (i.e. does understanding action-related language induce a facilitation or an interference with the corresponding action?), the time course, and the nature of the interaction (i.e. under what conditions does the phenomenon occur?) are largely unclear. To further explore this topic we exploited a go/no-go paradigm in which healthy participants were required to perform arm reaching movements toward a target when verbs expressing either hand or foot actions were shown, and to refrain from moving when abstract verbs were presented. We found that reaction times (RT) and percentages of errors increased when the verb involved the same effector used to give the response. This interference occurred very early, when the interval between verb presentation and the delivery of the go signal was 50 ms, and could be elicited until this delay was about 600 ms. In addition, RTs were faster when subjects used the right arm than when they used the left arm, suggesting that action–verb understanding is left-lateralized. Furthermore, when the color of the printed verb and not its meaning was the cue for movement execution the differences between RTs and error percentages between verb categories disappeared, unequivocally indicating that the phenomenon occurs only when the semantic content of a verb has to be retrieved. These results are compatible with the theory of embodied language, which hypothesizes that comprehending verbal descriptions of actions relies on an internal simulation of the sensory–motor experience of the action, and provide a new and detailed view of the interplay between action language and motor acts

How Are ‘Barack Obama’ and ‘President Elect’ Differentially Stored in the Brain? An ERP Investigation on the Processing of Proper and Common Noun Pairs

BACKGROUND:One of the most debated issues in the cognitive neuroscience of language is whether distinct semantic domains are differentially represented in the brain. Clinical studies described several anomic dissociations with no clear neuroanatomical correlate. Neuroimaging studies have shown that memory retrieval is more demanding for proper than common nouns in that the former are purely arbitrary referential expressions. In this study a semantic relatedness paradigm was devised to investigate neural processing of proper and common nouns. METHODOLOGY/PRINCIPAL FINDINGS:780 words (arranged in pairs of Italian nouns/adjectives and the first/last names of well known persons) were presented. Half pairs were semantically related ("Woody Allen" or "social security"), while the others were not ("Sigmund Parodi" or "judicial cream"). All items were balanced for length, frequency, familiarity and semantic relatedness. Participants were to decide about the semantic relatedness of the two items in a pair. RTs and N400 data suggest that the task was more demanding for common nouns. The LORETA neural generators for the related-unrelated contrast (for proper names) included the left fusiform gyrus, right medial temporal gyrus, limbic and parahippocampal regions, inferior parietal and inferior frontal areas, which are thought to be involved in the conjoined processing a familiar face with the relevant episodic information. Person name was more emotional and sensory vivid than common noun semantic access. CONCLUSIONS/SIGNIFICANCE:When memory retrieval is not required, proper name access (conspecifics knowledge) is not more demanding. The neural generators of N400 to unrelated items (unknown persons and things) did not differ as a function of lexical class, thus suggesting that proper and common nouns are not treated differently as belonging to different grammatical classes