This is the overview paper for the Deep Video Understanding (DVU) Grand Challenge. In recent years, a growing trend towards working on understanding videos (in particular movies) to a deeper level started to motivate researchers working in multimedia and computer vision to present new approaches and datasets to tackle this problem. This is a challenging research area which aims to develop a deep understanding of the relations which exist between different individuals and entities in movies using all available modalities such as video, audio, text and metadata. The aim of this grand challenge is to foster innovative research in this new direction and to provide benchmarking evaluations to advance technologies in the deep video understanding community.

This is the introduction paper to the 2nd International Workshop on Deep Video Understanding, organized at the 24th ACM International Conference on Multimodal Interaction. In recent years, a growing trend towards working on understanding videos (in particular movies) to a deeper level started to motivate researchers working in multimedia and computer vision to present new approaches and datasets to tackle this problem. This is a challenging research area which aims to develop a deep understanding of the relations which exist between different individuals and entities in movies using all available modalities such as video, audio, text and metadata. The aim of this workshop is to foster innovative research in this new direction and to provide benchmarking evaluations to advance technologies in the deep video understanding community.

This is the overview paper for the Deep Video Understanding (DVU) Grand Challenge. In recent years, a growing trend towards working on understanding videos (in particular movies) to a deeper level started to motivate researchers working in multimedia and computer vision to present new approaches and datasets to tackle this problem. This is a challenging research area which aims to develop a deep understanding of the relations which exist between different individuals and entities in movies using all available modalities such as video, audio, text and metadata. The aim of this grand challenge is to foster innovative research in this new direction and to provide benchmarking evaluations to advance technologies in the deep video understanding community.

This is the introduction paper to the International Workshop on Deep Video Understanding, organized at the 22nd ACM Interational Conference on Multimodal Interaction. In recent years, a growing trend towards working on understanding videos (in particular movies) to a deeper level started to motivate researchers working in multimedia and computer vision to present new approaches and datasets to tackle this problem. This is a challenging research area which aims to develop a deep understanding of the relations which exist between different individuals and entities in movies using all available modalities such as video, audio, text and metadata. The aim of this workshop is to foster innovative research in this new direction and to provide benchmarking evaluations to advance technologies in the deep video understanding community.

In this paper we propose a new evaluation challenge and direction in the area of High-level Video Understanding. The challenge we are proposing is designed to test automatic video analysis and understanding, and how accurately systems can comprehend a movie in terms of actors, entities, events and their relationship to each other. A pilot High-Level Video Understanding (HLVU) dataset of open source movies were collected for human assessors to build a knowledge graph representing each of them. A set of queries will be derived from the knowledge graph to test systems on retrieving relationships among actors, as well as reasoning and retrieving non-visual concepts. The objective is to benchmark if a computer system can "understand" non-explicit but obvious relationships the same way humans do when they watch the same movies. This is long-standing problem that is being addressed in the text domain and this project moves similar research to the video domain. Work of this nature is foundational to future video analytics and video understanding technologies. This work can be of interest to streaming services and broadcasters hoping to provide more intuitive ways for their customers to interact with and consume video content.

We present a novel method for the generation of automatic video summaries of academic presentations. We base our investigation on a corpus of multimodal academic conference presentations combining transcripts with paralinguistic multimodal features. We first generate summaries based on keywords by using transcripts created using automatic speech recognition (ASR). Start and end times for each spoken phrase are identified from the ASR transcript, then a value for each phrase created. Spoken phrases are then augmented by incorporating scores for human annotation of paralinguistic features. These features measure audience engagement, comprehension and speaker emphasis. We evaluate the effectiveness of summaries generated for individual presentations, created using speech transcripts and paralinguistic multimodal features, by performing eye-tracking evaluation of participants as they watch summaries and full presentations, and by questionnaire of participants upon completion of eye-tracking studies. Summaries were also evaluated for effectiveness by performing comparisons with an enhanced digital video browser.

Understanding audience comprehension levels for presentations has the potential to enable richer and more focused interaction with audio-visual recordings. We describe an investigation into automated analysis and classification of audience comprehension during academic presentations. We identify audio and visual features considered to most aid audience understanding. To obtain gold standards for comprehension levels, human annotators watched contiguous video segments from a corpus of academic presentations and estimated how much they understood or comprehended the content. We investigate pre-fusion and post-fusion strategies over a number of input streams and demonstrate the most effective modalities for classification of comprehension. We demonstrate that it is possible to build a classifier to predict potential audience comprehension levels, obtaining accuracy over a 7-class range of 52.9%, and over a binary classification problem to 85.4%.

Understanding audience engagement levels for presentations has the potential to enable richer and more focused interaction with audio-visual recordings. We describe an investigation into automated analysis of multimodal recordings of scientific talks where the use of modalities most typically associated with engagement such as eye-gaze is not feasible. We first study visual and acoustic features to identify those most commonly associated with good speaking techniques. To understand audience interpretation of good speaking techniques, we angaged human annotators to rate the qualities of the speaker for a series of 30-second video segments taken from a corpus of 9 hours of presentations from an academic conference. Our annotators also watched corresponding video recordings of the audience to presentations to estimate the level of audience engagement for each talk. We then explored the effectiveness of multimodal features extracted from the presentation video against Likert-scale ratings of each speaker as assigned by the annotators. and on manually labelled audience engagement levels. These features were used to build a classifier to rate the qualities of a new speaker. This was able classify a rating for a presenter over an 8-class range with an accuracy of 52%. By combining these classes to a 4-class range accuracy increases to 73%. We analyse linear correlations with individual speaker-based modalities and actual audience engagement levels to understand the corresponding effect on audience engagement. A further classifier was then built to predict the level of audience engagement to a presentation by analysing the speaker's use of acoustic and visual cues. Using these speaker based modalities pre-fused with speaker ratings only, we are able to predict actual audience engagement levels with an accuracy of 68%. By combining with basic visual features from the audience as whole, we are able to improve this to an accuracy of 70%.

SUMMARY: Although the 1000 Genomes haplotypes are the most commonly used reference panel for imputation, medical sequencing projects are generating large alternate sets of sequenced samples. Imputation in African Americans using 3384 haplotypes from the Exome Sequencing Project, compared with 2184 haplotypes from 1000 Genomes Project, increased effective sample size by 8.3-11.4% for coding variants with minor allele frequency <1%. No loss of imputation quality was observed using a panel built from phenotypic extremes. We recommend using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2's two-panel combination. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.