Visual7W: Grounded Question Answering in Images

We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.Comment: CVPR 201

Revamping question answering with a semantic approach over world knowledge

Classic textual question answering (QA) approaches that rely on statistical keyword relevance scoring without exploiting semantic content are useful to a certain extent, but are limited to questions answered by a small text excerpt. With the maturation of Wikipedia and with upcoming projects like DBpedia, we feel that nowadays QA can adopt a deeper, semantic approach to the task, where answers can be inferred using knowledge bases to overcome the limitations of textual QA approaches. In GikiCLEF, a QA-flavoured evaluation task, the best performing systems followed a semantic approach. In this paper, we present our motivations for preferring semantic approaches to QA over textual approaches, with Wikipedia serving as a raw knowledge source

Higher CMM levels attained by QA certified software developers

[Abstract]: This paper addresses the question: is higher capability maturity associated with adoption of Quality Assurance (QA) certification? To assess the extent of adoption of third-party QA certification by Australian software developers, a survey of 1,000 software developers was recently conducted. The questionnaire also included an assessment of their capability maturity based on the capability maturity model (CMM). Cynics who criticise the value of QA certification may be surprised by the strong association found between adoption of QA certification and capability maturity

Australian software developers embrace quality Assurance Certification

[Abstract]: This paper details a research project undertaken to assess the extent of adoption of quality assurance (QA) certification by Australian software developers. A brief history of government QA policy, the catalyst in the sudden interest in certification, is included. Primary data for the study were gathered from a survey of 1,000 Australian software developers, and were used to determine the extent of adoption of QA certification by Australian developers, their organisational characteristics, capability maturity and perceptions regarding the value of QA certification. Secondary data from the JAS-ANZ register of certified organisations enabled validation of survey responses and extrapolation of QA certification adoption. Major findings of the study revealed that 11 percent of respondents are certified to ISO 9001 or AS 3563, seven percent are in progress and 21 percent plan to adopt QA certification. It also revealed that specialist developers are adopting QA certification at twice the rate of in-house developers. Other factors found to be associated with adoption of QA certification are large development groups, developers with government or overseas clients, organisations with whole- or part-foreign ownership, and organisations undertaking corporate TQM initiatives. From the findings, detailed implications are drawn for managers and policy analysts