This paper presents the ideas for the 2021 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum-CLEF Labs 2021 in Bucharest, Romania. ImageCLEF is an ongoing evaluation initiative (active since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2021, the 19th edition of ImageCLEF will organize four main tasks: (i) a Medical task addressing visual question answering, a concept annotation and a tuberculosis classification task, (ii) a Coral task addressing the annotation and localisation of substrates in coral reef images, (iii) a DrawnUI task addressing the creation of websites from either a drawing or a screenshot by detecting the different elements present on the design and a new (iv) Aware task addressing the prediction of real-life consequences of online photo sharing. The strong participation in 2020, despite the COVID pandemic,
In this paper, we report on the creation of a publicly available, common evaluation framework for image and video visual interestingness prediction. We propose a robust data set, the Interestingness10k, with 9,831 images and more than 4 hours of video, interestigness scores determined based on more than 1M pair-wise annotations of 800 trusted annotators, some pre-computed multi-modal descriptors, and 192 system output results as baselines. The data were validated extensively during the 2016-2017 MediaEval benchmark campaigns. We provide an in-depth analysis of the crucial components of visual interestingness prediction algorithms by reviewing the capabilities and the evolution of the MediaEval benchmark systems, as well as of prominent systems from the literature. We discuss overall trends, influence of the employed features and techniques, generalization capabilities and the reliability of results. We also discuss the possibility of going beyond state-of-the-art performance via an automatic, ad-hoc system fusion, and propose a deep MLP-based architecture that outperforms the current state-of-theart systems by a large margin. Finally, we provide the most important lessons learned and insights gained.
This paper presents an overview of the ImageCLEF 2022 lab that was organized as part of the Conference and Labs of the Evaluation Forum -CLEF Labs 2022. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2022, the 20th edition of ImageCLEF runs four main tasks: (i) a medical task that groups two previous tasks, i.e., caption analysis and tuberculosis prediction, (ii) a social media aware task on estimating potential real-life effects of online image sharing, (iii) a nature coral task about segmenting and labeling collections of coral reef images, and (iv) a new fusion task addressing the design of late fusion schemes for boosting the performance, with two real-world applications: image search diversification (retrieval) and prediction of visual interestingness (regression). The benchmark campaign received the participation of over 25 groups submitting more than 258 runs.
While ensemble systems and late fusion mechanisms have proven their effectiveness by achieving state-of-the-art results in various computer vision tasks, current approaches are not exploiting the power of deep neural networks as their primary ensembling algorithm, but only as inducers, i.e., systems that are used as inputs for the primary ensembling algorithm. In this paper, we propose several deep neural network architectures as ensembling algorithms with various network configurations that use dense and attention layers, an input pre-processing algorithm, and a new type of deep neural network layer denoted the Cross-Space-Fusion layer, that further improves the overall results. Experimental validation is carried out on several data sets from various domains (emotional content classification, medical data captioning) and under various evaluation conditions (two-class regression, binary classification, and multi-label classification), proving the efficiency of DeepFusion.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.