no code implementations • ECCV 2020 • Yanbei Chen, Loris Bazzani
Interactive image retrieval is an emerging research topic with the objective of integrating inputs from multiple modalities as query for retrieval, e. g., textual feedback from users to guide, modify or refine image retrieval.
1 code implementation • 23 Nov 2022 • Yuchen Ma, Yanbei Chen, Zeynep Akata
In this work, we formulate a new knowledge distillation framework to transfer the knowledge from self-supervised pre-trained models to any other student network by a novel approach named Embedding Graph Alignment.
1 code implementation • 19 Oct 2022 • Abhra Chaudhuri, Massimiliano Mancini, Yanbei Chen, Zeynep Akata, Anjan Dutta
Representation learning for sketch-based image retrieval has mostly been tackled by learning embeddings that discard modality-specific information.
no code implementations • 24 Aug 2022 • Yanbei Chen, Massimiliano Mancini, Xiatian Zhu, Zeynep Akata
Semi-supervised learning and unsupervised learning offer promising paradigms to learn from an abundance of unlabeled visual data.
1 code implementation • 14 Jul 2022 • Uddeshya Upadhyay, Shyamgopal Karthik, Yanbei Chen, Massimiliano Mancini, Zeynep Akata
Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates.
1 code implementation • 27 Apr 2022 • Ilke Cugu, Massimiliano Mancini, Yanbei Chen, Zeynep Akata
Generalizing visual recognition models trained on a single distribution to unseen input distributions (i. e. domains) requires making them robust to superfluous correlations in the training set.
1 code implementation • 12 Apr 2022 • Andrei Neculai, Yanbei Chen, Zeynep Akata
Without bells and whistles, we show that our probabilistic model formulation significantly outperforms existing related methods on multimodal image retrieval while generalizing well to query with different amounts of inputs given in arbitrary visual and (or) textual modalities.
1 code implementation • NeurIPS 2021 • Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata
Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs.
1 code implementation • 29 Jun 2021 • Uddeshya Upadhyay, Yanbei Chen, Tobias Hepp, Sergios Gatidis, Zeynep Akata
However, the state-of-the-art GAN-based frameworks do not estimate the uncertainty in the predictions made by the network that is essential for making informed medical decisions and subsequent revision by medical experts and has recently been shown to improve the performance and interpretability of the model.
no code implementations • 4 May 2021 • Yanbei Chen, Thomas Hummel, A. Sophia Koepke, Zeynep Akata
Recent advances in XAI provide explanations for models trained on still images.
Explainable artificial intelligence Multimodal Deep Learning
1 code implementation • CVPR 2021 • Yanbei Chen, Yongqin Xian, A. Sophia Koepke, Ying Shan, Zeynep Akata
Having access to multi-modal cues (e. g. vision and audio) empowers some cognitive tasks to be done faster compared to learning from a single modality.
1 code implementation • 23 Feb 2021 • Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata
Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner.
1 code implementation • CVPR 2020 • Yanbei Chen, Shaogang Gong, Loris Bazzani
In this work, we tackle this task by a novel Visiolinguistic Attention Learning (VAL) framework.
Ranked #8 on Image Retrieval on Fashion IQ
no code implementations • ICCV 2019 • Yanbei Chen, Xiatian Zhu, Shaogang Gong
To tackle this limitation, we propose a novel Instance-Guided Context Rendering scheme, which transfers the source person identities into diverse target domain contexts to enable supervised re-id model learning in the unlabelled target domain.
1 code implementation • 31 Jan 2019 • Davide Gerosa, Sizheng Ma, Kaze W. K. Wong, Emanuele Berti, Richard O'Shaughnessy, Yanbei Chen, Krzysztof Belczynski
We use population synthesis simulations of isolated binary stars to explore some of the stellar physics that could be constrained with multiband events, and we show that specific formation pathways might be overrepresented in multiband events compared to ground-only detections.
High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology
1 code implementation • ECCV 2018 • Yanbei Chen, Xiatian Zhu, Shaogang Gong
We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data.
1 code implementation • 22 Aug 2018 • Yanbei Chen, Xiatian Zhu, Shaogang Gong
In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training.
Ranked #5 on Person Re-Identification on PRID2011
no code implementations • 29 Jun 2018 • Rhondale Tso, Davide Gerosa, Yanbei Chen
The early inspiral of massive stellar-mass black-hole binaries merging in LIGO's sensitivity band will be detectable at low frequencies by the upcoming space mission LISA.
General Relativity and Quantum Cosmology High Energy Astrophysical Phenomena