[Apologies for multiple postings]

ImageCLEFfusion (2nd edition) Registration: https://www.imageclef.org/2023/fusion Run submission: May 10, 2023 Working notes submission: June 5, 2023 CLEF 2023 conference: September 18-21, Thessaloniki, Greece

*** CALL FOR PARTICIPATION *** While deep neural networks have proven their predictive power in many tasks, there are still several domains where a single deep learning network is not enough for attaining high precision, e.g., prediction of subjective concepts such as violence, memorability, etc.

Late fusion, also called ensembling or decision-level fusion, represents one of the approaches that researchers employ to increase the performance of single-system approaches. It consists of using a series of weaker learner methods called inducers, whose prediction outputs are combined in the final step, via a fusion mechanism to create a new and improved super predictor. These systems have a long history and are shown to be particularly useful in scenarios where the performance of single-system approaches is not considered satisfactory.

The task challenges participants to develop and benchmark late fusion schemes. This task would allow to explore various aspects of late fusion mechanisms, such as the performance of different fusion methods, the methods for selecting inducers from a larger set, the exploitation of positive and negative correlations between inducers, and so on.

*** TASK *** The participants will receive a data set of real inducers and are expected to provide a fusion mechanism that would allow to combine them into a super-system yielding superior performance compared to the highest performing individual system. The provided inducers were developed to solve three real tasks: (i) prediction of visual interestingness (int --- regression task), (ii) diversification of image search results (div --- retrieval task), (iii) medical image captioning (cap --- multi-class labeling task).

*** DATA SET *** ImageCLEFfusion-int. The data for this task is extracted and corresponds to the Interestingness10k dataset. We will provide output data from 33 inducers, while 1,826 samples will be used for the development set, and 609 samples will be used for the testing set. ImageCLEFfusion-div. The data for this task is extracted and corresponds to the Retrieving Diverse Social Images Task dataset. We will provide outputs data from 117 inducers, while 104 queries will be used for the development set, and 35 samples will be used for the testing set. ImageCLEFfusion-cap. The data for this task is extracted from the ImageCLEFmedical Caption task. We will provide output data from 85 inducers, while 5,700 images will be used for the development set, and 1900 images will be used for the testing set.

*** METRICS *** Evaluation will be performed using the metrics specific to each dataset we use, e.g., MAP@10, F1@20, ClusterRecall@20, accuracy.

*** IMPORTANT DATES *** - Run submission: May 10, 2023 - Working notes submission: June 5, 2023 - CLEF 2023 conference: September 18-21, Thessaloniki, Greece (https://clef2023.clef-initiative.eu/)

*** OVERALL COORDINATION *** Liviu-Daniel Stefan, Politehnica University of Bucharest, Romania Mihai Gabriel Constantin, Politehnica University of Bucharest, Romania Mihai Dogariu, Politehnica University of Bucharest, Romania Bogdan Ionescu, Politehnica University of Bucharest, Romania

*** ACKNOWLEDGEMENT *** The task is supported under the H2020 AI4Media “A European Excellence Centre for Media, Society and Democracy” project, contract #951911 https://www.ai4media.eu/.

On behalf of the Organizers,

Bogdan Ionescu https://www.AIMultimediaLab.ro/