no code implementations • 27 Sep 2023 • Martin Nicolas Everaert, Athanasios Fitsios, Marco Bocchio, Sami Arpa, Sabine Süsstrunk, Radhakrishna Achanta
This enables us to generate images with more varied brightness, and images that better match a desired style or color.
no code implementations • ICCV 2023 • Martin Nicolas Everaert, Marco Bocchio, Sami Arpa, Sabine Süsstrunk, Radhakrishna Achanta
Not adapting this initial latent tensor to the style makes fine-tuning slow, expensive, and impractical, especially when only a few target style images are available.
1 code implementation • 23 May 2022 • Steven Stalder, Nathanaël Perraudin, Radhakrishna Achanta, Fernando Perez-Cruz, Michele Volpi
These attributions are provided in the form of masks that only show the classifier-relevant parts of an image, masking out the rest.
no code implementations • 16 Apr 2021 • Radhakrishna Achanta, Natasa Tagasovska
We show how our approach can be used for estimating uncertainty in prediction and out-of-distribution detection.
no code implementations • 2 Feb 2019 • Fayez Lahoud, Radhakrishna Achanta, Pablo Márquez-Neila, Sabine Süsstrunk
To obtain similar binary networks, existing methods rely on the sign activation function.
1 code implementation • 11 Sep 2018 • Majed El Helou, Frederike Dümbgen, Radhakrishna Achanta, Sabine Süsstrunk
Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among others.
2 code implementations • ECCV 2018 • Edo Collins, Radhakrishna Achanta, Sabine Süsstrunk
We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images.
Ranked #6 on Unsupervised Human Pose Estimation on Tai-Chi-HD
Unsupervised Facial Landmark Detection Unsupervised Human Pose Estimation +1
1 code implementation • 19 Feb 2018 • Ruofan Zhou, Radhakrishna Achanta, Sabine Süsstrunk
By training on high-quality samples, our deep residual demosaicing and super-resolution network is able to recover high-quality super-resolved images from low-resolution Bayer mosaics in a single step without producing the artifacts common to such processing when the two operations are done separately.
no code implementations • CVPR 2017 • Nikolaos Arvanitopoulos, Radhakrishna Achanta, Sabine Susstrunk
Reflections are a common artifact in images taken through glass windows.
no code implementations • CVPR 2017 • Radhakrishna Achanta, Sabine Susstrunk
We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation.
no code implementations • 27 Nov 2016 • Radhakrishna Achanta, Pablo Márquez-Neila, Pascal Fua, Sabine Süsstrunk
Since information is a natural way of measuring image complexity, our proposed algorithm leads to image segments that are smaller and denser in areas of high complexity and larger in homogeneous regions, thus simplifying the image while preserving its details.