The MediaEval 2016 Emotional Impact of Movies Task

Volume: 1739 Host publication title: MediaEval 2016 Multimedia Benchmark Workshop Host publication sub-title: Working Notes Proceedings of the MediaEval 2016 WorkshopNon peer reviewe

MediaEval 2016 Predicting Media Interestingness Task

Volume: 1739 Host publication title: MediaEval 2016 Multimedia Benchmark Workshop Host publication sub-title: Working Notes Proceedings of the MediaEval 2016 WorkshopNon peer reviewe

Exploiting multimedia in creating and analysing multimedia Web archives

The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general

Cultural Event Recognition with Visual ConvNets and Temporal Models

This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme. We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge. We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes. The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores. We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM. Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data. Our system achieved the second best result in the ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.Comment: Initial version of the paper accepted at the CVPR Workshop ChaLearn Looking at People 201

Adapting Binary Information Retrieval Evaluation Metrics for Segment-based Retrieval Tasks

This report describes metrics for the evaluation of the effectiveness of segment-based retrieval based on existing binary information retrieval metrics. This metrics are described in the context of a task for the hyperlinking of video segments. This evaluation approach re-uses existing evaluation measures from the standard Cranfield evaluation paradigm. Our adaptation approach can in principle be used with any kind of effectiveness measure that uses binary relevance, and for other segment-baed retrieval tasks. In our video hyperlinking setting, we use precision at a cut-off rank n and mean average precision.Comment: Explanation of evaluation measures for the linking task of the MediaEval Workshop 201

Method for run time hardware code profiling for algorithm acceleration

In this paper we propose a method for run time profiling of applications on instruction level by analysis of loops. Instead of looking for coarse grain blocks we concentrate on fine grain but still costly blocks in terms of execution times. Most code profiling is done in software by introducing code into the application under profile witch has time overhead, while in this work data for the position of a loop, loop body, size and number of executions is stored and analysed using a small non intrusive hardware block. The paper describes the system mapping to runtime reconfigurable systems. The fine grain code detector block synthesis results and its functionality verification are also presented in the paper. To demonstrate the concept MediaBench multimedia benchmark running on the chosen development platform is use

Overview of The MediaEval 2022 Predicting Video Memorability Task

This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022. This year we have reorganised and simplified the task in order to lubricate a greater depth of inquiry. Similar to last year, two datasets are provided in order to facilitate generalisation, however, this year we have replaced the TRECVid2019 Video-to-Text dataset with the VideoMem dataset in order to remedy underlying data quality issues, and to prioritise short-term memorability prediction by elevating the Memento10k dataset as the primary dataset. Additionally, a fully fledged electroencephalography (EEG)-based prediction sub-task is introduced. In this paper, we outline the core facets of the task and its constituent sub-tasks; describing the datasets, evaluation metrics, and requirements for participant submissions.Comment: 6 pages. In: MediaEval Multimedia Benchmark Workshop Working Notes, 202

Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition

This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with the state-of-the-art method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the 'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.Comment: Accepted for Sound and Music Computing (SMC 2017