Sequential labeling with structural SVM under an average precision loss

© Springer International Publishing AG 2016. The average precision (AP) is an important and widelyadopted performance measure for information retrieval and classification systems. However, owing to its relatively complex formulation, very few approaches have been proposed to learn a classifier by maximising its average precision over a given training set. Moreover, most of the existing work is restricted to i.i.d. data and does not extend to sequential data. For this reason, we herewith propose a structural SVM learning algorithm for sequential labeling that maximises an average precision measure. A further contribution of this paper is an algorithm that computes the average precision of a sequential classifier at test time, making it possible to assess sequential labeling under this measure. Experimental results over challenging datasets which depict human actions in kitchen scenarios (i.e., TUM Kitchen and CMU Multimodal Activity) show that the proposed approach leads to an average precision improvement of up to 4.2 and 5.7% points against the runner-up, respectively

Combining Sources of Description for Approximating Music Similarity Ratings

In this paper, we compare the effectiveness of basic acoustic features and genre annotations when adapting a music similarity model to user ratings. We use the Metric Learning to Rank algorithm to learn a Mahalanobis metric from comparative similarity ratings in in the MagnaTagATune database. Using common formats for feature data, our approach can easily be transferred to other existing databases. Our results show that genre data allow more effective learning of a metric than simple audio features, but a combination of both feature sets clearly outperforms either individual set

Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions

Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training. In this paper we try to overcome this difficulty by presenting a multi-utility learning framework for structured prediction that can learn from training instances with different forms of supervision. We propose a unified technique for inferring the loss functions most suitable for quantifying the consistency of solutions with the given weak annotation. We demonstrate the effectiveness of our framework on the challenging semantic image segmentation problem for which a wide variety of annotations can be used. For instance, the popular training datasets for semantic segmentation are composed of images with hard-to-generate full pixel labellings, as well as images with easy-to-obtain weak annotations, such as bounding boxes around objects, or image-level labels that specify which object categories are present in an image. Experimental evaluation shows that the use of annotation-specific loss functions dramatically improves segmentation accuracy compared to the baseline system where only one type of weak annotation is used

Human Aldehyde Dehydrogenase 3A1 (ALDH3A1) Exhibits Chaperone-Like Function

Aldehyde dehydrogenase 3A1 (ALDH3A1) is a metabolic enzyme that catalyzes the oxidation of various aldehydes. Certain types of epithelial tissues in mammals, especially those continually exposed to environmental stress (e.g., corneal epithelium), express ALDH3A1 at high levels and its abundance in such tissues is perceived to help to maintain cellular homeostasis under conditions of oxidative stress. Metabolic as well as non-metabolic roles for ALDH3A1 have been associated with its mediated resistance to cellular oxidative stress. In this study, we provide evidence that ALDH3A1 exhibits molecular chaperone-like activity further supporting its multifunctional role. Specifically, we expressed and purified the human ALDH3A1 in E. coli and used the recombinant protein to investigate its in vitro ability to protect SmaI and citrate synthase (from precipitation and/or deactivation) under thermal stress conditions. Our results indicate that recombinant ALDH3A1 exhibits significant chaperone function in vitro. Furthermore, over-expression of the fused histidine-tagged ALDH3A1 confers host E. coli cells with enhanced resistance to thermal shock, while ALDH3A1 over-expression in the human corneal cell line HCE-2 was sufficient for protecting them from the cytotoxic effects of both hydrogen peroxide and tert-butyl hydroperoxide. These results further support the chaperone-like function of human ALDH3A1. Taken together, ALDH3A1, in addition to its primary metabolic role in fundamental cellular detoxification processes, appears to play an essential role in protecting cellular proteins against aggregation under stress conditions

Training linear ranking SVMs in linearithmic time using red-black trees

We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(m*s+m*log(m)) time complexity, where m is the number of training examples, and s the average number of non-zero features per example. Best previously known training algorithms achieve the same efficiency only for restricted special cases, whereas the proposed approach allows any real valued utility scores in the training data. Experiments demonstrate the superior scalability of the proposed approach, when compared to the fastest existing RankSVM implementations.Comment: 20 pages, 4 figure

Automatic annotation of tennis games: An integration of audio, vision, and learning

Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges. In this paper we describe our approach to this task, which integrates computer vision, machine listening, and machine learning. At the low level processing, we improve upon our previously proposed state-of-the-art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events. At high level analysis, we model event classification as a sequence labelling problem, and investigate four machine learning techniques using simulated event sequences. Finally, we evaluate our proposed approach on three real world tennis games, and discuss the interplay between audio, vision and learning. To the best of our knowledge, our system is the only one that can annotate tennis game at such a detailed level

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Cross-modal subspace learning with scheduled adaptive margin constraints

This work has been partially funded by the CMU Portugal research project GoLocal Ref. CMUP-ERI/TIC/0046/2014, by the H2020 ICT project COGNITUS with the grant agreement no 687605 and by the FCT project NOVA LINCS Ref. UID/CEC/04516/2019. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.Cross-modal embeddings, between textual and visual modalities, aim to organise multimodal instances by their semantic correlations. State-of-the-art approaches use maximum-margin methods, based on the hinge-loss, to enforce a constant margin m, to separate projections of multimodal instances from different categories. In this paper, we propose a novel scheduled adaptive maximum-margin (SAM) formulation that infers triplet-specific constraints during training, therefore organising instances by adaptively enforcing inter-category and inter-modality correlations. This is supported by a scheduled adaptive margin function, that is smoothly activated, replacing a static margin by an adaptively inferred one reflecting triplet-specific semantic correlations while accounting for the incremental learning behaviour of neural networks to enforce category cluster formation and enforcement. Experiments on widely used datasets show that our model improved upon state-of-the-art approaches, by achieving a relative improvement of up to approximate to 12.5% over the second best method, thus confirming the effectiveness of our scheduled adaptive margin formulation.publishersversionpublishe