7 citations · 21 across the 9 of their papers we have counts for
12 papers
Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation
Nadine Behrmann, S. Alireza Golestaneh, Zico Kolter +2
This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to cur…
Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives
David T. Hoffmann, Nadine Behrmann, Juergen Gall +2
This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. In contr…
TaylorSwiftNet: Taylor Driven Temporal Modeling for Swift Future Frame Prediction
Saber Pourheydari, Emad Bahrami, Mohsen Fayyaz +3
While recurrent neural networks (RNNs) demonstrate outstanding capabilities for future video frame prediction, they model dynamics in a discrete time space, i.e., they predict the…
Long Short View Feature Decomposition via Contrastive Video Representation Learning
Nadine Behrmann, Mohsen Fayyaz, Juergen Gall +1
Self-supervised video representation methods typically focus on the representation of temporal attributes in videos. However, the role of stationary versus non-stationary attribute…
Self-labeled Conditional GANs
Mehdi Noroozi
This paper introduces a novel and fully unsupervised framework for conditional GAN training in which labels are automatically obtained from data. We incorporate a clustering networ…
Unsupervised Video Representation Learning by Bidirectional Feature Prediction
Nadine Behrmann, Juergen Gall, Mehdi Noroozi
This paper introduces a novel method for self-supervised video representation learning via feature prediction. In contrast to the previous methods that focus on future feature pred…
3D CNNs with Adaptive Temporal Feature Resolutions
Mohsen Fayyaz, Emad Bahrami, Ali Diba +4
While state-of-the-art 3D Convolutional Neural Networks (CNN) achieve very good results on action recognition datasets, they are computationally very expensive and require many GFL…
Boosting Self-Supervised Learning via Knowledge Transfer
Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro +1
In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer th…
Reflection Separation and Deblurring of Plenoptic Images
Paramanand Chandramouli, Mehdi Noroozi, Paolo Favaro
In this paper, we address the problem of reflection removal and deblurring from a single image captured by a plenoptic camera. We develop a two-stage approach to recover the scene…
Representation Learning by Learning to Count
Mehdi Noroozi, Hamed Pirsiavash, Paolo Favaro
We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an…
Motion Deblurring in the Wild
Mehdi Noroozi, Paramanand Chandramouli, Paolo Favaro
The task of image deblurring is a very ill-posed problem as both the image and the blur are unknown. Moreover, when pictures are taken in the wild, this task becomes even more chal…
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Mehdi Noroozi, Paolo Favaro
In this paper we study the problem of image representation learning without human annotation. By following the principles of self-supervision, we build a convolutional neural netwo…