This article presents a contest between the rating and the paired comparison voting in judging the perceived dominance of virtual characters, the aim being to select the voting mode that is the most convenient for voters while staying reliable. The com…
In this article, we report on the creation of a publicly available, common evaluation framework for Violent Scenes Detection (VSD) in Hollywood and YouTube videos. We propose a robust data set, the VSD96, with more than 96 hours of video of various genres, annotations at different levels of detail (e.g., shot-level, segment-level), annotations of mid-level concepts (e.g., blood, fire), various pre-computed multi-modal descriptors, and over 230 system output results as baselines. This is the most comprehensive data set available to this date tailored to the VSD task and was extensively validated during the MediaEval benchmarking campaigns. Furthermore, we provide an in-depth analysis of the crucial components of VSD algorithms, by reviewing the capabilities and the evolution of existing systems (e.g., overall trends and outliers, the influence of the employed features and fusion techniques, the influence of deep learning approaches). Finally, we discuss the possibility of going beyond state-of-the-art performance via an ad-hoc late fusion approach. Experimentation is carried out on the VSD96 data. We provide the most important lessons learned and gained insights. The increasing number of publications using the VSD96 data underline the importance of the topic. The presented and published resources are a practitioner’s guide and also a strong baseline to overcome, which will help researchers for the coming years in analyzing aspects of audio-visual affect and violence detection in movies and videos.
While the relationship between facial expressions and emotion has been a productive area of inquiry, research is only recently exploring whether a link exists between facial expressions and cognitive processes. Using findings from psychology and neuros…
Multimodal human sentiment comprehension refers to recognizing human affection from multiple modalities. There exist two key issues for this problem. First, it is difficult to explore time-dependent interactions between modalities and focus on the impo…
Hearings of witnesses and defendants play a crucial role when reaching court trial decisions. Given the high-stakes nature of trial outcomes, developing computational models that assist the decision-making process is an important research venue. In thi…
Continuous affect estimation from facial expressions has attracted increased attention in the affective computing research community. This paper presents a principled framework for estimating continuous affect from video sequences. Based on recent deve…
Presents the introductory editorial for this issue of the publication.