22 citations · 45 across the 6 of their papers we have counts for
8 papers · 1 filter
SCVRL: Shuffled Contrastive Video Representation Learning
Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli +2
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learni…
VidTr: Video Transformer Without Convolutions
Yanyi Zhang, Xinyu Li, Chunhui Liu +6
We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal infor…
Unsupervised Behaviour Analysis and Magnification (uBAM) using Deep Learning
Biagio Brattoli, Uta Buechler, Michael Dorkenwald +5
Motor behaviour analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused…
Sharing Matters for Generalization in Deep Metric Learning
Timo Milbich, Karsten Roth, Biagio Brattoli +1
Learning the similarity between images constitutes the foundation for numerous vision tasks. The common paradigm is discriminative metric learning, which seeks an embedding that se…
Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
Biagio Brattoli, Joseph Tighe, Fedor Zhdanov +2
Trained on large datasets, deep learning (DL) can accurately classify videos into hundreds of diverse classes. However, video data is expensive to annotate. Zero-shot learning (ZSL…
MIC: Mining Interclass Characteristics for Improved Metric Learning
Karsten Roth, Biagio Brattoli, Björn Ommer
Metric learning seeks to embed images of objects suchthat class-defined relations are captured by the embeddingspace. However, variability in images is not just due to different de…
Cross and Learn: Cross-Modal Self-Supervision
Nawid Sayed, Biagio Brattoli, Björn Ommer
In this paper we present a self-supervised method for representation learning utilizing two different modalities. Based on the observation that cross-modal information has a high s…
Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning
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. As surrogate task, we jointly…