A Dataset and Baselines for Visual Question Answering on Art

Answering questions related to art pieces (paintings) is a difficult task, as it implies the understanding of not only the visual information that is shown in the picture, but also the contextual knowledge that is acquired through the study of the history of art. In this work, we introduce our first attempt towards building a new dataset, coined AQUA (Art QUestion Answering). The question-answer (QA) pairs are automatically generated using state-of-the-art question generation methods based on paintings and comments provided in an existing art understanding dataset. The QA pairs are cleansed by crowdsourcing workers with respect to their grammatical correctness, answerability, and answers' correctness. Our dataset inherently consists of visual (painting-based) and knowledge (comment-based) questions. We also present a two-branch model as baseline, where the visual and knowledge questions are handled independently. We extensively compare our baseline model against the state-of-the-art models for question answering, and we provide a comprehensive study about the challenges and potential future directions for visual question answering on art

Preventing and Treating Women’s Postpartum Depression: A Qualitative Systematic Review on Partner-Inclusive Interventions

Partner-related factors associated with the occurrence of Postpartum Depression (PPD) may justify the partner’s inclusion in preventive and treatment approaches. The aim of this qualitative systematic review was to synthesize the literature on partner-inclusive interventions designed to prevent or treat postpartum depression (PPD) in women. In accordance with the PRISMA guidelines, the systematic search of studies published between 1967 and May 2015 in PsycINFO and PubMed identified 26 studies that met the inclusion criteria, which reported on 24 interventions. The following partner parameters were analyzed: participation type, session content, mental health assessment, attendance assessment, and the effects of partner’s participation on the women’s response to the interventions. Total participation by the partner was mostly reported in the prevention studies, whereas partial participation was reported in the treatment studies. The session content was mostly based on psychoeducation about PPD and parenthood, coping strategies to facilitate the transition to parenthood such as the partner’s emotional and instrumental support, and problem-solving and communication skills. Some benefits perceived by the couples underscore the relevance of the partner’s inclusion in PPD interventions. However, the scarce information about the partner’s attendance and the associated effects on the women’s intervention outcomes, along with methodological limitations of the studies, made it difficult to determine if the partner’s participation was associated with the intervention’s efficacy. Conclusions about the clinical value of including partners in PPD interventions are still limited. More research is warranted to better inform health policy strategies

AusTraits, a curated plant trait database for the Australian flora

We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge

Weakly supervised instance segmentation by learning annotation consistent instances

Recent approaches for weakly supervised instance segmentations depend on two components: (i) a pseudo label generation model which provides instances that are consistent with a given annotation; and (ii) an instance segmentation model, which is trained in a supervised manner using the pseudo labels as ground-truth. Unlike previous approaches, we explicitly model the uncertainty in the pseudo label generation process using a conditional distribution. The samples drawn from our conditional distribution provide accurate pseudo labels due to the use of semantic class aware unary terms, boundary aware pairwise smoothness terms, and annotation aware higher order terms. Furthermore, we represent the instance segmentation model as an annotation agnostic prediction distribution. In contrast to previous methods, our representation allows us to define a joint probabilistic learning objective that minimizes the dissimilarity between the two distributions. Our approach achieves state of the art results on the PASCAL VOC 2012 data set, outperforming the best baseline by 4.2% mAP0.5 and 4.8% mAP0.75