S-LOR: Supervised Stream Learning for Object Recognition.

In a stream learning scenario, where new data arrives at a slow pace, it is crucial to leverage new knowledge at the same rate without losing prior knowledge, and without assuming data stationarity. This scenario presents a significant challenge for incremental learning, particularly for tasks such as object recognition in video streams. In this paper, a novel approach is proposed that uses a set of weak classifiers that evolves into ensembles to enhance the generalization power of the system, as new video subsequences of the same instances are presented. We evaluate the efficiency of our approach and compare with state-of-the-art methods using a benchmark dataset. The code is available at https://github.com/vilaB/object_recognition .