no code implementations • 11 Apr 2021 • Wei Chen, Yu Liu, Erwin M. Bakker, Michael S. Lew
Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy.
1 code implementation • CVPR 2021 • Nan Pu, Wei Chen, Yu Liu, Erwin M. Bakker, Michael S. Lew
In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains.
no code implementations • 27 Jan 2021 • Wei Chen, Yu Liu, Weiping Wang, Erwin Bakker, Theodoros Georgiou, Paul Fieguth, Li Liu, Michael S. Lew
In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics.
no code implementations • 16 Oct 2020 • Wei Chen, Weiping Wang, Li Liu, Michael S. Lew
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text.
1 code implementation • 6 Aug 2020 • Nan Pu, Wei Chen, Yu Liu, Erwin M. Bakker, Michael S. Lew
To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively.
no code implementations • ICCV 2017 • Yu Liu, Yanming Guo, Erwin M. Bakker, Michael S. Lew
A major challenge in matching between vision and language is that they typically have completely different features and representations.
no code implementations • 16 Nov 2016 • Yu Liu, Yanming Guo, Michael S. Lew
Despite recent advances in multi-scale deep representations, their limitations are attributed to expensive parameters and weak fusion modules.
no code implementations • CVPR 2016 • Yu Liu, Michael S. Lew
We consider these false positives in the supervision, and are able to achieve high performance for better edge detection.
no code implementations • 31 Dec 2010 • E. R. Gast, Michael S. Lew
We present a head tracker which is a combination of a optical flow and a template based tracker.