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Extensive experimental results show that training on the automatically generated optical flow outperforms methods that are trained on rigid synthetic data using FlowNet-S, LiteFlowNet, PWC-Net, PWC-Net, and RAFT.
This work proposes albedo and shading gradient descriptors which are derived from physics-based models and is the first to directly address the texture and intensity ambiguity problems of the shading estimations.
This survey provides a comprehensive evaluation of various deep learning-based segmentation architectures, covering a wide range of models, from traditional ones like FCN and PSPNet to more modern approaches like SegFormer and FAN, and proposes to evaluate the methods in terms of temporal consistency and corruption vul...
This work proposes an e-ective pre-training strategy, namely Temporal Masked Auto-Encoders (T-MAE), which takes as input temporally adjacent frames and learns temporal dependency, and demonstrates that T-MAE achieves the best performance on both Waymo and ONCE datasets among competitive self-supervised approaches.
It is concluded that adversarial training is beneficial if and only if the reconstruction loss is not too constrained, and non-adversarial training outperforms (or is on par with) any method trained with a GAN when a constrained reconstruction loss is used in combination with batch normalisation.
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