Search Results for author:
Found 13 papers, 6 papers with code
Not Every Tree Is a Forest: Benchmarking Forest Types from Satellite Remote Sensing
Developing accurate and reliable models for forest types mapping is critical to support efforts for halting deforestation and for biodiversity conservation (such as European Union Deforestation Regulation (EUDR)).
Heterogeneous graph neural networks for species distribution modeling
For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model.
PaliGemma: A versatile 3B VLM for transfer
PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model.
Planted: a dataset for planted forest identification from multi-satellite time series
In this paper, we present a dataset consisting of data from five public satellites for recognizing forest plantations and planted tree species across the globe.
Simple Open-Vocabulary Object Detection with Vision Transformers
Combining simple architectures with large-scale pre-training has led to massive improvements in image classification.
Ranked #1 on
One-Shot Object Detection
on MS COCO
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
Multi-Label Image Classification
on BigEarthNet
(mAP (macro) metric)
A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
And, how close are we to general visual representations?
Ranked #7 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 #17 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
