23 citations · 33 across the 8 of their papers we have counts for
5 papers
PatchDropout: Economizing Vision Transformers Using Patch Dropout
Yue Liu, Christos Matsoukas, Fredrik Strand +2
Vision transformers have demonstrated the potential to outperform CNNs in a variety of vision tasks. But the computational and memory requirements of these models prohibit their us…
An analysis of over-sampling labeled data in semi-supervised learning with FixMatch
Miquel Martí i Rabadán, Sebastian Bujwid, Alessandro Pieropan +2
Most semi-supervised learning methods over-sample labeled data when constructing training mini-batches. This paper studies whether this common practice improves learning and how. W…
Persistent Evidence of Local Image Properties in Generic ConvNets
Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki +3
Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary informatio…
Factors of Transferability for a Generic ConvNet Representation
Hossein Azizpour, Ali Sharif Razavian, Josephine Sullivan +2
Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual recognition tasks. In the common scenario, a ConvNet is…
Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach
Hossein Azizpour, Stefan Carlsson
Computer vision tasks are traditionally defined and evaluated using semantic categories. However, it is known to the field that semantic classes do not necessarily correspond to a…