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Found 5 papers, 2 papers with code
AdaMAE: Adaptive Masking for Efficient Spatiotemporal Learning with Masked Autoencoders
Our adaptive masking strategy samples visible tokens based on the semantic context using an auxiliary sampling network.
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MaskTune: Mitigating Spurious Correlations by Forcing to Explore
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features.
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy.
Scan2Cap: Context-aware Dense Captioning in RGB-D Scans
We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors.
Complex-valued Imaging with Total Variation Regularization: An Application to Full-Waveform Inversion in Visco-acoustic Media
Since estimating a complex frequency-dependent velocity at each grid point in space is not realistic, the optimization is generally performed in the real domain by processing the phase velocity (or slowness) at a reference frequency and attenuation (or quality factor) as separate real parameters.
