1 code implementation • 3 Jun 2022 • Andrés C. Rodríguez, Stefano D'Aronco, Rodrigo Caye Daudt, Jan D. Wegner, Konrad Schindler
Illustrations contained in field guides deliberately focus on discriminative properties of each species, and can serve as side information to transfer knowledge from seen to unseen bird species.
1 code implementation • 31 May 2022 • Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz, Mina Rezaei, Bernd Bischl, Rodrigo Caye Daudt, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
We show that the idea can be extended to uncertainty quantification: by modulating the network activations of a single deep network with FiLM, one obtains a model ensemble with high diversity, and consequently well-calibrated estimates of epistemic uncertainty, with low computational overhead in comparison.
1 code implementation • CVPR 2022 • Riccardo de Lutio, Alexander Becker, Stefano D'Aronco, Stefania Russo, Jan D. Wegner, Konrad Schindler
With the decision to employ the source as a constraint rather than only as an input to the prediction, our method differs from state-of-the-art deep architectures for guided super-resolution, which produce targets that, when downsampled, will only approximately reproduce the source.
no code implementations • 10 Feb 2022 • Stefano D'Aronco, Giorgio Trumpy, David Pfluger, Jan Dirk Wegner
We validate the proposed method on a lenticular film dataset and compare it to other approaches.
no code implementations • 7 Jun 2021 • Riccardo de Lutio, Yihang She, Stefano D'Aronco, Stefania Russo, Philipp Brun, Jan D. Wegner, Konrad Schindler
Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts.
no code implementations • 24 May 2021 • Andrés C. Rodríguez, Stefano D'Aronco, Konrad Schindler, Jan D. Wegner
To that end, we propose a new, active deep learning method to estimate oil palm density at large scale from Sentinel-2 satellite images, and apply it to generate complete maps for Malaysia and Indonesia.
1 code implementation • ICLR 2021 • Yujia Liu, Stefano D'Aronco, Konrad Schindler, Jan Dirk Wegner
Next, the corners are linked with an exhaustive set of candidate edges, which is again pruned to obtain the final wireframe.
1 code implementation • 17 Feb 2021 • Mehmet Ozgur Turkoglu, Stefano D'Aronco, Gregor Perich, Frank Liebisch, Constantin Streit, Konrad Schindler, Jan Dirk Wegner
The three-level label hierarchy is encoded in a convolutional, recurrent neural network (convRNN), such that for each pixel the model predicts three labels at different level of granularity.
1 code implementation • 4 Dec 2020 • Nando Metzger, Mehmet Ozgur Turkoglu, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
We propose to use neural ordinary differential equations (NODEs) in combination with RNNs to classify crop types in irregularly spaced image sequences.
1 code implementation • 25 Aug 2020 • Vít Růžička, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
We investigate active learning in the context of deep neural network models for change detection and map updating.
no code implementations • 23 Mar 2020 • Ahmed Samy Nassar, Stefano D'Aronco, Sébastien Lefèvre, Jan D. Wegner
In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object.
1 code implementation • 20 Mar 2020 • Andres C. Rodriguez, Stefano D'Aronco, Konrad Schindler, Jan Dirk Wegner
We propose a scheme for supervised image classification that uses privileged information, in the form of keypoint annotations for the training data, to learn strong models from small and/or biased training sets.
3 code implementations • 25 Nov 2019 • Mehmet Ozgur Turkoglu, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM and GRU while being more robust against vanishing or exploding gradients.
2 code implementations • ICCV 2019 • Riccardo de Lutio, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
Guided super-resolution is a unifying framework for several computer vision tasks where the inputs are a low-resolution source image of some target quantity (e. g., perspective depth acquired with a time-of-flight camera) and a high-resolution guide image from a different domain (e. g., a grey-scale image from a conventional camera); and the target output is a high-resolution version of the source (in our example, a high-res depth map).