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20172021
most citedXNOR-Net++: Improved Binary Neural Networks

118 citations · 232 across the 17 of their papers we have counts for

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cs.CV2021

SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021

Swathikiran Sudhakaran, Adrian Bulat, Juan-Manuel Perez-Rua +5

This report presents the technical details of our submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021. To participate in the challenge we deployed spatio-temporal…

cs.CV2021

WarpedGANSpace: Finding non-linear RBF paths in GAN latent space

Christos Tzelepis, Georgios Tzimiropoulos, Ioannis Patras

This work addresses the problem of discovering, in an unsupervised manner, interpretable paths in the latent space of pretrained GANs, so as to provide an intuitive and easy way of…

cs.CV202157 cited

Space-time Mixing Attention for Video Transformer

Adrian Bulat, Juan-Manuel Perez-Rua, Swathikiran Sudhakaran +2

This paper is on video recognition using Transformers. Very recent attempts in this area have demonstrated promising results in terms of recognition accuracy, yet they have been al…

cs.CV2021

Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition

Enrique Sanchez, Mani Kumar Tellamekala, Michel Valstar +1

Temporal context is key to the recognition of expressions of emotion. Existing methods, that rely on recurrent or self-attention models to enforce temporal consistency, work on the…

cs.CV2021

Improving memory banks for unsupervised learning with large mini-batch, consistency and hard negative mining

Adrian Bulat, Enrique Sánchez-Lozano, Georgios Tzimiropoulos

An important component of unsupervised learning by instance-based discrimination is a memory bank for storing a feature representation for each training sample in the dataset. In t…

cs.CV2020

Semi-supervised Facial Action Unit Intensity Estimation with Contrastive Learning

Enrique Sanchez, Adrian Bulat, Anestis Zaganidis +1

This paper tackles the challenging problem of estimating the intensity of Facial Action Units with few labeled images. Contrary to previous works, our method does not require to ma…