1 code implementation • IEEE Access 2023 • Ala Shaabana, Zahra Gharaee, Paul Fieguth
Secondly, classifiers trained by a single, monolithic neural network often lack stability and generalization.
no code implementations • 16 Feb 2024 • Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley
Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning.
no code implementations • 26 Sep 2023 • Amir Nazemi, Mohammad Javad Shafiee, Zahra Gharaee, Paul Fieguth
We propose two novel techniques to reduce the memory requirement of Online VOS methods while improving modeling accuracy and generalization on long videos.
1 code implementation • NeurIPS 2023 • Zahra Gharaee, ZeMing Gong, Nicholas Pellegrino, Iuliia Zarubiieva, Joakim Bruslund Haurum, Scott C. Lowe, Jaclyn T. A. McKeown, Chris C. Y. Ho, Joschka McLeod, Yi-Yun C Wei, Jireh Agda, Sujeevan Ratnasingham, Dirk Steinke, Angel X. Chang, Graham W. Taylor, Paul Fieguth
In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-Insect Dataset.
1 code implementation • 17 Feb 2023 • Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley
First, we propose a novel causal model that explains the generative factors in motion forecasting datasets using features that are common across all environments and with features that are specific to each environment.
no code implementations • 14 Feb 2023 • Zahra Gharaee, Felix Järemo Lawin, Per-Erik Forssén
We designed a network to generate a proxy ground-truth heatmap from a set of keypoints distributed all over the category-specific mean shape, where each is represented by a unique color on a labeled texture.
no code implementations • 4 Nov 2022 • Nicholas Pellegrino, Zahra Gharaee, Paul Fieguth
The BIOSCAN project, led by the International Barcode of Life Consortium, seeks to study changes in biodiversity on a global scale.
no code implementations • 9 Feb 2022 • Zahra Gharaee
Based on the experiments of this article, applying internally simulated perceptual states represented by action pattern vectors improves the performance of the recognition task in all experiments.
1 code implementation • 16 Jul 2021 • Zahra Gharaee, Shreyas Kowshik, Oliver Stromann, Michael Felsberg
We present a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks.
no code implementations • 30 Apr 2021 • Zahra Gharaee
The proposed action recognition architecture of this thesis is composed of several processing layers including a preprocessing layer, an ordered vector representation layer and three layers of neural networks.
1 code implementation • 23 Apr 2021 • Zahra Gharaee
Automatic recognition of an online series of unsegmented actions requires a method for segmentation that determines when an action starts and when it ends.
1 code implementation • 22 Apr 2021 • Zahra Gharaee
Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network.
1 code implementation • 13 Apr 2021 • Zahra Gharaee, Peter Gärdenfors, Magnus Johnsson
Human recognition of the actions of other humans is very efficient and is based on patterns of movements.
1 code implementation • 13 Apr 2021 • Zahra Gharaee, Peter Gärdenfors, Magnus Johnsson
The second information processing stream is carried out by a second system that determines which object among several in the agent's vicinity the action is applied to.
1 code implementation • International Conference on Patern Recognition (ICPR 2020) 2021 • Zahra Gharaee, Karl Holmquist, Linbo He, Michael Felsberg
We trained our system using both ground truth and estimated semantic segmentation input.