A task category space for user-centric comparative multimedia search evaluations

In the last decade, user-centric video search competitions have facilitated the evolution of interactive video search systems. So far, these competitions focused on a small number of search task categories, with few attempts to change task category configurations. Based on our extensive experience with interactive video search contests, we have analyzed the spectrum of possible task categories and propose a list of individual axes that define a large space of possible task categories. Using this concept of category space, new user-centric video search competitions can be designed to benchmark video search systems from different perspectives. We further analyse the three task categories considered so far at the Video Browser Showdown and discuss possible (but sometimes challenging) shifts within the task category spac

A task category space for user-centric comparative multimedia search evaluations

In the last decade, user-centric video search competitions have facilitated the evolution of interactive video search systems. So far, these competitions focused on a small number of search task categories, with few attempts to change task category configurations. Based on our extensive experience with interactive video search contests, we have analyzed the spectrum of possible task categories and propose a list of individual axes that define a large space of possible task categories. Using this concept of category space, new user-centric video search competitions can be designed to benchmark video search systems from different perspectives. We further analyse the three task categories considered so far at the Video Browser Showdown and discuss possible (but sometimes challenging) shifts within the task category spac

Dublin City University participation in the VTT track at TRECVid 2017

Dublin City University participated in the video-to-text caption generation task in TRECVid and this paper describes the three approaches we took for our 4 submitted runs. The first approach is based on extracting regularly-spaced keyframes from a video, generating a text caption for each keyframe and then combining the keyframe captions into a single caption. The second approach is based on detecting image crops from those keyframes using saliency map to include as much of the attractive part of the image as possible, generating a caption for each crop in each keyframe, and combining the captions into one. The third approach is an end-to-end system, a true deep learning submission based on MS-COCO, an externally available set of training captions. The paper presents a description and the official results of each of the approaches

An overview on the evaluated video retrieval tasks at TRECVID 2022

The TREC Video Retrieval Evaluation (TRECVID) is a TREC-style video analysis and retrieval evaluation with the goal of promoting progress in research and development of content-based exploitation and retrieval of information from digital video via open, tasks-based evaluation supported by metrology. Over the last twenty-one years this effort has yielded a better understanding of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID has been funded by NIST (National Institute of Standards and Technology) and other US government agencies. In addition, many organizations and individuals worldwide contribute significant time and effort. TRECVID 2022 planned for the following six tasks: Ad-hoc video search, Video to text captioning, Disaster scene description and indexing, Activity in extended videos, deep video understanding, and movie summarization. In total, 35 teams from various research organizations worldwide signed up to join the evaluation campaign this year. This paper introduces the tasks, datasets used, evaluation frameworks and metrics, as well as a high-level results overview.Comment: arXiv admin note: substantial text overlap with arXiv:2104.13473, arXiv:2009.0998