Search Results for author: Biagio Brattoli

Found 8 papers, 3 papers with code

VidTr: Video Transformer Without Convolutions

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.

Action Classification Action Recognition

Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning

no code implementations16 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.

Sharing Matters for Generalization in Deep Metric Learning

no code implementations12 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.

Metric Learning

MIC: Mining Interclass Characteristics for Improved Metric Learning

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 #15 on Metric Learning on CUB-200-2011 (using extra training data)

Image Retrieval Metric Learning

Cross and Learn: Cross-Modal Self-Supervision

1 code implementation9 Nov 2018 Nawid Sayed, Biagio Brattoli, Björn Ommer

In this paper we present a self-supervised method for representation learning utilizing two different modalities.

Action Recognition Optical Flow Estimation +2

Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning

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.

General Classification reinforcement-learning +3

Cannot find the paper you are looking for? You can Submit a new open access paper.