We introduce IndEgo, a multimodal egocentric and exocentric dataset addressing common industrial tasks, including assembly/disassembly, logistics and organisation, inspection and repair, woodworking, and others. The dataset contains 3,460 egocentric recordings (approximately 197 hours), along with 1,092 exocentric recordings (approximately 97 hours). A key focus of the dataset is collaborative work, where two workers jointly perform cognitively and physically intensive tasks. The egocentric recordings include rich multimodal data and added context via eye gaze, narration, sound, motion, and others. We provide detailed annotations (actions, summaries, mistake annotations, narrations), metadata, processed outputs (eye gaze, hand pose, semi-dense point cloud), and benchmarks on procedural and non-procedural task understanding, Mistake Detection, and reasoning-based Question Answering. Baseline evaluations for Mistake Detection, Question Answering and collaborative task understanding show that the dataset presents a challenge for the state-of-the-art multimodal models. Our dataset is available at: https://huggingface.co/datasets/FraunhoferIPK/IndEgo

Incremental Learning (IL) aims to develop Machine Learning (ML) models that can learn from continuous streams of data and mitigate catastrophic forgetting. We analyse the current state-of-the-art Class-IL implementations and demonstrate why the current body of research tends to be one-dimensional, with an excessive focus on accuracy metrics. A realistic evaluation of Continual Learning methods should also emphasise energy consumption and overall computational load for a comprehensive understanding. This paper addresses research gaps between current IL research and industrial project environments, including varying incremental tasks and the introduction of Joint Training in tandem with IL. We introduce InVar100 (Industrial Objects in Varied Contexts), a novel dataset meant to simulate the visual environments in industrial setups and perform various experiments for IL. Additionally, we incorporate explainability (using class activations) to interpret the model predictions. Our approach, RECIL (Real-World Scenarios and Energy Efficiency Considerations for Class Incremental Learning) provides meaningful insights about the applicability of IL approaches in practical use cases. The overarching aim is to bring the Incremental Learning and Green AI fields together and encourage the application of CIL methods in real-world scenarios. Code and dataset are available.