1 code implementation • CVPR 2024 • Jakub Micorek, Horst Possegger, Dominik Narnhofer, Horst Bischof, Mateusz Kozinski
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this distribution with a neural network.
Ranked #1 on Anomaly Detection on CUHK Avenue
1 code implementation • 18 Mar 2024 • M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin, Sivan Doveh, Jakub Micorek, Mateusz Kozinski, Hilde Kuehne, Horst Possegger
Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language Models (VLMs).
1 code implementation • 30 May 2023 • Stefan Leitner, M. Jehanzeb Mirza, Wei Lin, Jakub Micorek, Marc Masana, Mateusz Kozinski, Horst Possegger, Horst Bischof
We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i. e. test time) when the respective weather conditions are encountered.
1 code implementation • ICCV 2023 • Wei Lin, Leonid Karlinsky, Nina Shvetsova, Horst Possegger, Mateusz Kozinski, Rameswar Panda, Rogerio Feris, Hilde Kuehne, Horst Bischof
We adapt a VL model for zero-shot and few-shot action recognition using a collection of unlabeled videos and an unpaired action dictionary.
Ranked #3 on Zero-Shot Action Recognition on Charades
1 code implementation • CVPR 2023 • Wei Lin, Muhammad Jehanzeb Mirza, Mateusz Kozinski, Horst Possegger, Hilde Kuehne, Horst Bischof
Our proposed method demonstrates a substantial performance gain over existing test-time adaptation approaches in both evaluations of a single distribution shift and the challenging case of random distribution shifts.
1 code implementation • CVPR 2023 • Muhammad Jehanzeb Mirza, Pol Jané Soneira, Wei Lin, Mateusz Kozinski, Horst Possegger, Horst Bischof
Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time.
1 code implementation • ICCV 2023 • M. Jehanzeb Mirza, Inkyu Shin, Wei Lin, Andreas Schriebl, Kunyang Sun, Jaesung Choe, Horst Possegger, Mateusz Kozinski, In So Kweon, Kun-Jin Yoon, Horst Bischof
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
1 code implementation • 14 Jul 2022 • Doruk Oner, Hussein Osman, Mateusz Kozinski, Pascal Fua
Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes.
no code implementations • 29 Sep 2021 • Doruk Oner, Adélie Garin, Mateusz Kozinski, Kathryn Hess, Pascal Fua
Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results.
no code implementations • ECCV 2020 • Subeesh Vasu, Mateusz Kozinski, Leonardo Citraro, Pascal Fua
Instead, we use a more sophisticated discriminator that returns a label pyramid describing what portions of the road network are correct at several different scales.
no code implementations • 9 May 2019 • Agata Mosinska, Mateusz Kozinski, Pascal Fua
Detection of curvilinear structures in images has long been of interest.
no code implementations • CVPR 2018 • Agata Mosinska, Pablo Marquez-Neila, Mateusz Kozinski, Pascal Fua
We propose a new loss term that is aware of the higher-order topological features of linear structures.
no code implementations • CVPR 2015 • Mateusz Kozinski, Raghudeep Gadde, Sergey Zagoruyko, Guillaume Obozinski, Renaud Marlet
We present a new shape prior formalism for segmentation of rectified facade images.