CVPR 2023 - DistractFlow: Improving Optical Flow Estimation via

In this episode we discuss DistractFlow: Improving Optical Flow Estimation via  Realistic Distractions and Pseudo Labeling by Authors: Jisoo Jeong, Hong Cai, Risheek Garrepalli, Fatih Porikli Affiliation: Qualcomm AI Research†. The paper proposes a novel data augmentation technique, called DistractFlow, for training optical flow estimation models. This approach introduces distractions to the input frames, using a mixing ratio to combine one of the frames in the pair with a distractor image depicting a similar domain. The distracted pairs allow the model to learn related variations and become robust against challenging deviations. The approach can be applied to training any optical flow estimation models and improves existing models, outperforming the latest state of the art on multiple benchmarks.