The world is not stationary. To thrive in evolving environments, humans are capable of continual acquisition and transfer of new knowledge, from a continuous stream of highly correlated visual stimuli over multiple tasks, while retaining previously learned experiences. In contrast to human learning, most AI systems struggle to recognise new objects when presented with a video stream of new object classes incrementally available over time. Meanwhile, they also fail to retain good performance on earlier learned classes without repeated passes over the data, known as catastrophic forgetting.  Our aim is to design a continual learning framework that recognises incrementally available object classes on video streams without repeated visits of the old data in a memory-efficient manner. With multi-disciplinary approaches at the intersection of neuroscience, cognitive science, and AI, we hope to gain a better understanding of the continual learning mechanisms in biological and artificial intelligence.