Voice Onset Time Enhanced User System (VOTEUS): a web graphic interface for the analysis of plosives’ release phases

The paper proposes an up-to-date literature review of the works using AutoVOT, a discriminative large-margin learning algorithm developed for the semi-automatic measurement of voice onset times. In order to expand the accessibility of the tool in linguistic research, we present VOTEUS, a user-friendly graphic interface written in Python. The interface is conceived to assist the researcher throughout the whole process of annotation, from the forced alignment of the corpora to the refinement of the AutoVOT tier and the extraction of the durations. The general aim is to speed up this phase of data analysis, providing a significant improvement on prevalent practice to date

Am I Done? Predicting Action Progress in Videos

In this paper we deal with the problem of predicting action progress in videos. We argue that this is an extremely important task since it can be valuable for a wide range of interaction applications. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. To provide a general definition of action progress, we ground our work in the linguistics literature, borrowing terms and concepts to understand which actions can be the subject of progress estimation. As a result, we define a categorization of actions and their phases. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets

Attribute disentanglement with gradient reversal for interactive fashion retrieval

Interactive fashion search is gaining more and more interest thanks to the rapid diffusion of online retailers. It allows users to browse fashion items and perform attribute manipulations, modifying parts or details of given garments. To successfully model and analyze garments at such a fine-grained level, it is necessary to obtain attribute-wise representations, separating information relative to different characteristics. In this work we propose an attribute disentanglement method based on attribute classifiers and the usage of gradient reversal layers. This combination allows us to learn attribute-specific features, removing unwanted details from each representation. We test the effectiveness of our learned features in a fashion attribute manipulation task, obtaining state of the art results. Furthermore, to favor training stability we present a novel loss balancing approach, preventing reversed losses to diverge during the optimization process

Indexing ensembles of exemplar-SVMs with rejecting taxonomies

Ensembles of Exemplar-SVMs have been used for a wide variety of tasks, such as object detection, segmentation, label transfer and mid-level feature learning. In order to make this technique effective though a large collection of classifiers is needed, which often makes the evaluation phase prohibitive. To overcome this issue we exploit the joint distribution of exemplar classifier scores to build a taxonomy capable of indexing each Exemplar-SVM and enabling a fast evaluation of the whole ensemble. We experiment with the Pascal 2007 benchmark on the task of object detection and on a simple segmentation task, in order to verify the robustness of our indexing data structure with reference to the standard Ensemble. We also introduce a rejection strategy to discard not relevant image patches for a more efficient access to the data