This work introduces Dialogue-AV, a benchmarking dataset for Audio-Video-Language (AVL). We propose using dialogue to describe video content instead of single captions, capturing nuances and shared meanings between audio and visual elements. This approach contributes significantly to improving the diversity of video descriptions and enables comprehensive evaluation of AVL learning across different downstream tasks, such as Cross-Modal Retrieval, Visual Question-Answering, and Video Captioning. Our dataset comprises approximately 258k audiovisual samples accompanied by dialogue-based descriptions for benchmarking. Dialogue-AV builds upon existing State-of-the-Art (SOTA) datasets that feature human-generated descriptions, enhancing them with model-generated ones that describe all modalities. We also present zero-shot baseline results utilising SOTA Visual-Language Models (VLMs), demonstrating that Dialogue-AV is capable of benchmarking a variety of downstream tasks with diverse inputs. Our key contributions include: 1) Dialogue-AV, a benchmark dataset for dialogue-based AVL models; and 2) benchmarks that expose the limitations of current SOTA VLMs. The code and dataset are accessible at: github.com/lvilaca16/dialogue-av.

We introduce VEMOCLAP: Video EMOtion Classifier using Pretrained features, the first readily available and open-source web application that analyzes the emotional content of any user-provided video. We improve our previous work, which exploits open-source pretrained models that work on video frames and audio, and then efficiently fuse the resulting pretrained features using multi-head cross-attention. Our approach increases the state-of-the-art classification accuracy on the Ekman-6 video emotion dataset by 4.3% and offers an online application for users to run our model on their own videos or YouTube videos. We invite the readers to try our application at serkansulun.com/app.

Immersive video applications impose unpractical bandwidth requirements for best-effort networks. With Multi-View (MV) streaming, these can be minimized by resorting to view prediction techniques. SmoothMV is a multi-view system that uses a non-intrusive head tracking mechanism to detect the viewer's interest and select appropriate views. By coupling Neural Networks (NNs) to anticipate the viewer's interest, a reduction of view-switching latency is likely to be obtained. The objective of this paper is twofold: 1) Present a solution for acquisition of gaze data from users when viewing MV content; 2) Describe a dataset, collected with a large-scale testbed, capable of being used to train NNs to predict the user's viewing interest. Tracking data from head movements was obtained from 45 participants using an Intel Realsense F200 camera, with 7 video playlists, each being viewed a minimum of 17 times. This dataset is publicly available to the research community and constitutes an important contribution to reducing the current scarcity of such data. Tools to obtain saliency/heat maps and generate complementary plots are also provided as an open-source software package.

Multimedia content production is nowadays widespread due to technological advances, namely supported by smartphones and social media. Although the massive amount of media content brings new opportunities to the industry, it also obfuscates the relevance of marketing content, meant to maintain and lure new audiences. This leads to an emergent necessity of producing these kinds of contents as quickly and engagingly as possible. Creating these automatically would decrease both the production costs and time, particularly by using static media for the creation of short storytelling animated clips. We propose an innovative approach that uses context and content information to transform a still photo into an appealing context-aware video clip. Thus, our solution presents a contribution to the state-of-the-art in computer vision and multimedia technologies and assists content creators with a value-added service to automatically build rich contextualized multimedia stories from single photographs.

Technological advances are pushing into the mass market innovative wearable devices featuring increasing processing and sensing capacity, non-intrusiveness and ubiquitous use. Sensors built-in those devices, enable acquiring different types of data and by taking advantage of the available processing power, it is possible to run intelligent applications that process the sensed data to offer added-value to the user in multiple domains. Although not new to the modern society, it is unquestionable that the present exercise boom is rapidly spreading across all age groups. However, in a great majority of cases, people perform their physical activity on their own, either due to time or budget constraints and may easily get discouraged if they do not see results or perform exercises inadequately. This paper presents an application, running on a wearable device, aiming at operating as a personal trainer that validates a set of proposed exercises in a sports session. The developed solution uses inertial sensors of an Android Wear smartwatch and, based on a set of pattern recognition algorithms, detects the rate of success in the execution of a planned workout. The fact that all processing can be executed on the device is a differentiator factor to other existing solutions.

This ACM International Workshop on Immersive Media Experiences is in its 3rd edition. Since 2013 in Barcelona, it has been a meeting point of researchers, students, media producers, service providers and industry players in the area of immersive media environments, applications and experiences. After the successful first edition at ACM Multimedia 2013 and the consolidation of the theme and the team at Orlando in 2014, ImmersiveMe'15 aims at bringing to the stage new ideas and developments that keep this topic as appealing as in the previous editions. ImmersiveMe'15 will now take place in Brisbane and, again, it will be a platform to present interesting and out-of-the-box new work that contributes to make the world more interactive, immersive and engaging.

The 2nd ACM International Workshop on Immersive Media Experiences (ImmersiveMe'14) at ACM Multimedia aims at bringing together researchers, students, media producers, service providers and industry players in the emergent area of immersive media experiences, through the exploration of different scenarios, applications, and neighboring fields. This second edition, after a successful first edition at ACM Multimedia 2013, provides a platform for presenting on-going work, to consolidate and tie different research communities working on this engaging area, as well as to point directions for the future.

Immersive media has the potential for strong impact on users' emotions and their sense of presence and engagement. The main objective of this workshop is to bring together researchers, students, media producers, service providers and industry players in the area of emergent immersive media. The workshop will provide a platform for a deep discussion on ongoing work, recent achievements and experiences. It is expected not only to consolidate experiences but also to identify aspects where strong collaboration among all the interested players is needed and to point towards future working directions.

Presents an integrated approach (practices) to object-oriented distributed application development that covers all phases of software development: requirements capture, analysis, design (structural and behavioural), simulation and targeting to CORBA-based distributed processing environments. We focus on a code generation strategy that addresses and supports all phases of the practices. We highlight code generation objectives including support for multiple paths through the practices, reuse of existing tool-sets and support for traceability. We describe one path in more detail and present an example of design-OMT-to-SDL translation based on labelling in OMT. We describe two development environments used in the project and analyse them with respect to their support for our code generation objectives. Our tool adaptation work is addressing identified weaknesses. This includes an SDL-to-Java/CORBA code generator, the generation of CORBA-to-SDL-mapping C++ wrappers for C code generated by existing tools and the use of CORBA to integrate development tools and support traceability.