642 citations · 3.9k across the 143 of their papers we have counts for
14 papers · 1 filter
Deep Matching Autoencoders
Tanmoy Mukherjee, Makoto Yamada, Timothy M. Hospedales
Increasingly many real world tasks involve data in multiple modalities or views. This has motivated the development of many effective algorithms for learning a common latent space…
The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching
Qian Yu, Xiaobin Chang, Yi-Zhe Song +2
Many vision problems require matching images of object instances across different domains. These include fine-grained sketch-based image retrieval (FG-SBIR) and Person Re-identific…
Learning to Compare: Relation Network for Few-Shot Learning
Flood Sung, Yongxin Yang, Li Zhang +3
We present a conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each. O…
Learning to Generalize: Meta-Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song +1
Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics. {Domain Generaliz…
Deeper, Broader and Artier Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song +1
The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generaliza…
iVQA: Inverse Visual Question Answering
Feng Liu, Tao Xiang, Timothy M. Hospedales +2
We propose the inverse problem of Visual question answering (iVQA), and explore its suitability as a benchmark for visuo-linguistic understanding. The iVQA task is to generate a qu…
Weakly Supervised Image Annotation and Segmentation with Objects and Attributes
Zhiyuan Shi, Yongxin Yang, Timothy M. Hospedales +1
We propose to model complex visual scenes using a non-parametric Bayesian model learned from weakly labelled images abundant on media sharing sites such as Flickr. Given weak image…
Scalable and Effective Deep CCA via Soft Decorrelation
Xiaobin Chang, Tao Xiang, Timothy M. Hospedales
Recently the widely used multi-view learning model, Canonical Correlation Analysis (CCA) has been generalised to the non-linear setting via deep neural networks. Existing deep CCA…
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning
Flood Sung, Li Zhang, Tao Xiang +2
We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples. Our framework is motivated by actor-critic reinforcement learning, but can…
Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images
Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang
We address the problem of localisation of objects as bounding boxes in images and videos with weak labels. This weakly supervised object localisation problem has been tackled in th…
Transferring a Semantic Representation for Person Re-Identification and Search
Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang
Learning semantic attributes for person re-identification and description-based person search has gained increasing interest due to attributes' great potential as a pose and view-i…
Actor-Critic Sequence Training for Image Captioning
Li Zhang, Flood Sung, Feng Liu +4
Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users…
Deep Mutual Learning
Ying Zhang, Tao Xiang, Timothy M. Hospedales +1
Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network. The typical application is to transfer from a powerful large…
Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation
Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang
We address the problem of localisation of objects as bounding boxes in images with weak labels. This weakly supervised object localisation problem has been tackled in the past usin…