activity
20192024
most citedEgocentric Action Recognition by Video Attention and Temporal Context

2 citations · 4 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2024

Move Anything with Layered Scene Diffusion

Jiawei Ren, Mengmeng Xu, Jui-Chieh Wu +3

Diffusion models generate images with an unprecedented level of quality, but how can we freely rearrange image layouts? Recent works generate controllable scenes via learning spati…

cs.CV2023★ 2 cited

Learning Garment DensePose for Robust Warping in Virtual Try-On

Aiyu Cui, Sen He, Tao Xiang +1

Virtual try-on, i.e making people virtually try new garments, is an active research area in computer vision with great commercial applications. Current virtual try-on methods usual…

cs.CV2021

Few-shot Action Recognition with Prototype-centered Attentive Learning

Xiatian Zhu, Antoine Toisoul, Juan-Manuel Perez-Rua +3

Few-shot action recognition aims to recognize action classes with few training samples. Most existing methods adopt a meta-learning approach with episodic training. In each episode…

cs.CV2020★ 2 cited

Egocentric Action Recognition by Video Attention and Temporal Context

Juan-Manuel Perez-Rua, Antoine Toisoul, Brais Martinez +4

We present the submission of Samsung AI Centre Cambridge to the CVPR2020 EPIC-Kitchens Action Recognition Challenge. In this challenge, action recognition is posed as the problem o…

cs.CV2020

Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention

Juan-Manuel Perez-Rua, Brais Martinez, Xiatian Zhu +3

Attentive video modeling is essential for action recognition in unconstrained videos due to their rich yet redundant information over space and time. However, introducing attention…

cs.LG2019

Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation

Jean Kossaifi, Antoine Toisoul, Adrian Bulat +3

Training deep neural networks with spatio-temporal (i.e., 3D) or multidimensional convolutions of higher-order is computationally challenging due to millions of unknown parameters…