Computer Vision and Image Understanding
Hoàng-Ân Lê, Tushar Nimbhorkar, Thomas Mensink et al.
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Computer Vision and Image Understanding
A. S. Baslamisli, Yang Liu, Sezer Karaoglu et al.
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Review
Computer Vision and Image Understanding
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...
Ronny Velastegui, Maxim Tatarchenko, Sezer Karaoglu et al.
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for Point Cloud Representation Learning
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.
Weijie Wei Fatemeh, Karimi Nejadasl, Theo Gevers et al.
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Computer Vision and Image Understanding
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.
R. Groenendijk, Sezer Karaoglu, Theo Gevers et al.
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