1,983 research outputs found

    Weakly Supervised Domain-Specific Color Naming Based on Attention

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    The majority of existing color naming methods focuses on the eleven basic color terms of the English language. However, in many applications, different sets of color names are used for the accurate description of objects. Labeling data to learn these domain-specific color names is an expensive and laborious task. Therefore, in this article we aim to learn color names from weakly labeled data. For this purpose, we add an attention branch to the color naming network. The attention branch is used to modulate the pixel-wise color naming predictions of the network. In experiments, we illustrate that the attention branch correctly identifies the relevant regions. Furthermore, we show that our method obtains state-of-the-art results for pixel-wise and image-wise classification on the EBAY dataset and is able to learn color names for various domains.Comment: Accepted at ICPR201

    Multimodal Visual Concept Learning with Weakly Supervised Techniques

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    Despite the availability of a huge amount of video data accompanied by descriptive texts, it is not always easy to exploit the information contained in natural language in order to automatically recognize video concepts. Towards this goal, in this paper we use textual cues as means of supervision, introducing two weakly supervised techniques that extend the Multiple Instance Learning (MIL) framework: the Fuzzy Sets Multiple Instance Learning (FSMIL) and the Probabilistic Labels Multiple Instance Learning (PLMIL). The former encodes the spatio-temporal imprecision of the linguistic descriptions with Fuzzy Sets, while the latter models different interpretations of each description's semantics with Probabilistic Labels, both formulated through a convex optimization algorithm. In addition, we provide a novel technique to extract weak labels in the presence of complex semantics, that consists of semantic similarity computations. We evaluate our methods on two distinct problems, namely face and action recognition, in the challenging and realistic setting of movies accompanied by their screenplays, contained in the COGNIMUSE database. We show that, on both tasks, our method considerably outperforms a state-of-the-art weakly supervised approach, as well as other baselines.Comment: CVPR 201

    Exercise Fails to Improve Neurocognition in Depressed Middle-Aged and Older Adults

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    Purpose: Although cross-sectional studies have demonstrated an association between higher levels of aerobic fitness and improved neurocognitive function, there have been relatively few interventional studies investigating this relationship, and results have been inconsistent. We assessed the effects of aerobic exercise on neurocognitive function in a randomized controlled trial of patients with major depressive disorder (MDD). Methods: Two-hundred and two sedentary men (n = 49) and women (n = 153), aged 40 yr and over and who met diagnostic criteria for MDD, were randomly assigned to the following: a) supervised exercise, b) home-based exercise, c) sertraline, or d) placebo pill. Before and after 4 months of treatment, participants completed measures of: Executive Function (Trail Making Test BA difference score, Stroop Color/Word, Ruff 2 & 7 Test, Digit Symbol), Verbal Memory (Logical Memory, Verbal Paired Associates), and Verbal Fluency/Working Memory (Animal Naming, Controlled Oral Word Association Test, Digit Span). Multivariate analyses of covariance were performed to test the effects of treatment on posttreatment neuropsychological test scores, with baseline neuropsychological test scores, age, education, and change in depression scores entered as covariates. Results: The performance of exercise participants was no better than participants receiving placebo across all neuropsychological tests. Exercise participants performed better than participants receiving sertraline on tests of executive function but not on tests of verbal memory or verbal fluency/ working memory. Conclusions: We found little evidence to support the benefits of an aerobic exercise intervention on neurocognitive performance in patients with MDD. Originally published Medicine and Science in Sport and Exercise, Vol. 40, No. 7, July 200

    Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation

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    Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains continuously without source data or target supervision. It has wide applications, such as robotic vision and autonomous driving. The main underlying challenge of CVDA is to learn helpful information only from the unsupervised target data while avoiding forgetting previously learned knowledge catastrophically, which is out of the capability of previous Video-based Unsupervised Domain Adaptation methods. Therefore, we propose a Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART) to address the challenge in CVDA. Firstly, to learn from unsupervised domains, we propose to learn from pseudo labels. However, in continuous adaptation, prediction errors can accumulate rapidly in pseudo labels, and CART effectively tackles this problem with two key modules. Specifically, The first module generates refined pseudo labels using model predictions and deploys a novel attentive learning strategy. The second module compares the outputs of augmented data from the current model to the outputs of weakly augmented data from the source model, forming a novel consistency regularization on the model to alleviate the accumulation of prediction errors. Extensive experiments suggest that the CVDA performance of CART outperforms existing methods by a considerable margin.Comment: 16 pages, 9 tables, 10 figure
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