no code implementations • CVPR 2023 • Jeongun Ryu, Aaron Valero Puche, Jaewoong Shin, Seonwook Park, Biagio Brattoli, Jinhee Lee, Wonkyung Jung, Soo Ick Cho, Kyunghyun Paeng, Chan-Young Ock, Donggeun Yoo, Sérgio Pereira
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images.
no code implementations • 24 May 2022 • Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe, Davide Modolo
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos.
no code implementations • ICCV 2021 • Yanyi Zhang, Xinyu Li, Chunhui Liu, Bing Shuai, Yi Zhu, Biagio Brattoli, Hao Chen, Ivan Marsic, Joseph Tighe
We first introduce the vanilla video transformer and show that transformer module is able to perform spatio-temporal modeling from raw pixels, but with heavy memory usage.
Ranked #15 on Action Classification on Charades
no code implementations • 16 Dec 2020 • Biagio Brattoli, Uta Buechler, Michael Dorkenwald, Philipp Reiser, Linard Filli, Fritjof Helmchen, Anna-Sophia Wahl, Bjoern Ommer
A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement.
no code implementations • 12 Apr 2020 • Timo Milbich, Karsten Roth, Biagio Brattoli, Björn Ommer
The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes.
1 code implementation • CVPR 2020 • Biagio Brattoli, Joseph Tighe, Fedor Zhdanov, Pietro Perona, Krzysztof Chalupka
Our training procedure builds on insights from recent video classification literature and uses a trainable 3D CNN to learn the visual features.
Ranked #4 on Zero-Shot Action Recognition on ActivityNet
2 code implementations • ICCV 2019 • Karsten Roth, Biagio Brattoli, Björn Ommer
In contrast, we propose to explicitly learn the latent characteristics that are shared by and go across object classes.
Ranked #19 on Metric Learning on CUB-200-2011 (using extra training data)
1 code implementation • 9 Nov 2018 • Nawid Sayed, Biagio Brattoli, Björn Ommer
In this paper we present a self-supervised method for representation learning utilizing two different modalities.
no code implementations • ECCV 2018 • Uta Büchler, Biagio Brattoli, Björn Ommer
Self-supervised learning of convolutional neural networks can harness large amounts of cheap unlabeled data to train powerful feature representations.
no code implementations • CVPR 2017 • Biagio Brattoli, Uta Buchler, Anna-Sophia Wahl, Martin E. Schwab, Bjorn Ommer
Behavior analysis provides a crucial non-invasive and easily accessible diagnostic tool for biomedical research.