Continuous Lifelong learning of high-dimensional data streams is a challenging research problem. In fact, fully retraining models each time new data become available is infeasible, due to computational and storage issues, while naive incremental strategies have been shown to suffer from catastrophic forgetting. In the context of real-world object recognition applications (e.g., robotic vision), where continuous learning is crucial, very few datasets and benchmarks are available to evaluate and compare emerging techniques. In this work we propose a new dataset and benchmark CORe50, specifically designed for continuous object recognition, and introduce baseline approaches for different continuous learning scenarios.
In Table we compare datasets benchmarks which, in our opinion, could be used for continuous object recognition. Datasets where temporal coherent sequences are not available (or cannot be generated from static frames) are here excluded. In principle, such datasets could be used for continuous learning as well, but we think that temporally coherent sequences allow a larger number of real-world applications to be addressed (e.g., robotic vision scenario). YouTube-8M @cite_9 provides a huge number of videos acquired in difficult natural settings. However, the classes are quite heterogeneous and acquisition conditions are completely uncontrolled in terms of object distance, pose, lighting, occlusions, etc. In other words, we believe it is too challenging for current continuous learning approaches (still in their infancy).
@cite_9
2524365899
This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place. Because the challenge provides pre-extracted visual and audio features instead of the raw videos, we mainly investigate various temporal modeling approaches to aggregate the frame-level features for multi-label video recognition. Our system contains three major components: two-stream sequence model, fast-forward sequence model and temporal residual neural networks. Experiment results on the challenging Youtube-8M dataset demonstrate that our proposed temporal modeling approaches can significantly improve existing temporal modeling approaches in the large-scale video recognition tasks. To be noted, our fast-forward LSTM with a depth of 7 layers achieves 82.75 in term of GAP@20 on the Kaggle Public test set.