Nadia Nasri

Eduardo Blanco-Fernández, Carlos Gutiérrez-Álvarez, Nadia Nasri et al.

Dense video captioning involves detecting and describing events within video sequences. Traditional methods operate in an offline setting, assuming the entire video is available for analysis. In contrast, in this work we introduce a groundbreaking paradigm: Live Video Captioning (LVC), where captions must be generated for video streams in an online manner. This shift brings unique challenges, including processing partial observations of the events and the need for a temporal anticipation of the actions. We formally define the novel problem of LVC and propose innovative evaluation metrics specifically designed for this online scenario, demonstrating their advantages over traditional metrics. To address the novel complexities of LVC, we present a new model that combines deformable transformers with temporal filtering, enabling effective captioning over video streams. Extensive experiments on the ActivityNet Captions dataset validate the proposed approach, showcasing its superior performance in the LVC setting compared to state-of-the-art offline methods. To foster further research, we provide the results of our model and an evaluation toolkit with the new metrics integrated at: https://github.com/gramuah/lvc.

Nadia Nasri, Carlos Gutiérrez-Álvarez, Sergio Lafuente-Arroyo et al.

Continual learning (CL) is crucial for evaluating adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forgetting, where models lose proficiency in previously learned tasks as they acquire new ones. While numerous solutions have been proposed, existing experimental setups often rely on idealized class-incremental learning scenarios. We introduce Realistic Continual Learning (RealCL), a novel CL paradigm where class distributions across tasks are random, departing from structured setups. We also present CLARE (Continual Learning Approach with pRE-trained models for RealCL scenarios), a pre-trained model-based solution designed to integrate new knowledge while preserving past learning. Our contributions include pioneering RealCL as a generalization of traditional CL setups, proposing CLARE as an adaptable approach for RealCL tasks, and conducting extensive experiments demonstrating its effectiveness across various RealCL scenarios. Notably, CLARE outperforms existing models on RealCL benchmarks, highlighting its versatility and robustness in unpredictable learning environments.