Search Results for author:
Found 3 papers, 2 papers with code
Improving Visual Representation Learning through Perceptual Understanding
We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features.
Ranked #2 on
Self-Supervised Image Classification
on ImageNet (finetuned)
(using extra training data)
Representation Learning
Self-Supervised Image Classification
Efficient On-the-fly Category Retrieval using ConvNets and GPUs
We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval - where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets.
Return of the Devil in the Details: Delving Deep into Convolutional Nets
In particular, we show that the data augmentation techniques commonly applied to CNN-based methods can also be applied to shallow methods, and result in an analogous performance boost.
