Search Results for author:
Found 9 papers, 4 papers with code
Simple Open-Vocabulary Object Detection with Vision Transformers
Combining simple architectures with large-scale pre-training has led to massive improvements in image classification.
Continental-Scale Building Detection from High Resolution Satellite Imagery
Identifying the locations and footprints of buildings is vital for many practical and scientific purposes.
Scaling Vision with Sparse Mixture of Experts
We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable and competitive with the largest dense networks.
Ranked #1 on
Few-Shot Image Classification
on ImageNet - 5-shot
Training general representations for remote sensing using in-domain knowledge
Automatically finding good and general remote sensing representations allows to perform transfer learning on a wide range of applications - improving the accuracy and reducing the required number of training samples.
AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification
The discovered attention cells can be seamlessly inserted into existing backbone networks, e. g., I3D or S3D, and improve video classification accuracy by more than 2% on both Kinetics-600 and MiT datasets.
In-domain representation learning for remote sensing
Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community.
Ranked #1 on
Image Classification
on RESISC45
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
And, how close are we to general visual representations?
Ranked #9 on
Image Classification
on VTAB-1k
(using extra training data)
The Visual Task Adaptation Benchmark
Representation learning promises to unlock deep learning for the long tail of vision tasks without expansive labelled datasets.
Progressive Neural Architecture Search
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.
Ranked #235 on
Image Classification
on ImageNet
