642 citations · 3.9k across the 143 of their papers we have counts for
16 papers · 1 filter
Disjoint Label Space Transfer Learning with Common Factorised Space
Xiaobin Chang, Yongxin Yang, Tao Xiang +1
In this paper, a unified approach is presented to transfer learning that addresses several source and target domain label-space and annotation assumptions with a single model. It i…
RelationNet2: Deep Comparison Columns for Few-Shot Learning
Xueting Zhang, Yuting Qiang, Flood Sung +2
Few-shot deep learning is a topical challenge area for scaling visual recognition to open ended growth of unseen new classes with limited labeled examples. A promising approach is…
Behavioural Repertoire via Generative Adversarial Policy Networks
Marija Jegorova, Stéphane Doncieux, Timothy Hospedales
Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpect…
Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice
Kunkun Pang, Mingzhi Dong, Yang Wu +1
Active learning aims to reduce annotation cost by predicting which samples are useful for a human teacher to label. However it has become clear there is no best active learning alg…
Deep Factorised Inverse-Sketching
Kaiyue Pang, Da Li, Jifei Song +3
Modelling human free-hand sketches has become topical recently, driven by practical applications such as fine-grained sketch based image retrieval (FG-SBIR). Sketches are clearly r…
Universal Perceptual Grouping
Ke Li, Kaiyue Pang, Jifei Song +4
In this work we aim to develop a universal sketch grouper. That is, a grouper that can be applied to sketches of any category in any domain to group constituent strokes/segments in…
Hypernetwork Knowledge Graph Embeddings
Ivana Balažević, Carl Allen, Timothy M. Hospedales
Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness. Inferring missing relations (links) between entities (nodes)…
Deep Neural Decision Trees
Yongxin Yang, Irene Garcia Morillo, Timothy M. Hospedales
Deep neural networks have been proven powerful at processing perceptual data, such as images and audio. However for tabular data, tree-based models are more popular. A nice propert…
Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning
Kunkun Pang, Mingzhi Dong, Yang Wu +1
Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to la…
What the Vec? Towards Probabilistically Grounded Embeddings
Carl Allen, Ivana Balažević, Timothy Hospedales
Word2Vec (W2V) and GloVe are popular, fast and efficient word embedding algorithms. Their embeddings are widely used and perform well on a variety of natural language processing ta…
Learning to Sketch with Shortcut Cycle Consistency
Jifei Song, Kaiyue Pang, Yi-Zhe Song +2
To see is to sketch -- free-hand sketching naturally builds ties between human and machine vision. In this paper, we present a novel approach for translating an object photo to a s…
Sketch-a-Classifier: Sketch-based Photo Classifier Generation
Conghui Hu, Da Li, Yi-Zhe Song +2
Contemporary deep learning techniques have made image recognition a reasonably reliable technology. However training effective photo classifiers typically takes numerous examples w…
Multi-Level Factorisation Net for Person Re-Identification
Xiaobin Chang, Timothy M. Hospedales, Tao Xiang
Key to effective person re-identification (Re-ID) is modelling discriminative and view-invariant factors of person appearance at both high and low semantic levels. Recently develop…
Learning Deep Sketch Abstraction
Umar Riaz Muhammad, Yongxin Yang, Yi-Zhe Song +2
Human free-hand sketches have been studied in various contexts including sketch recognition, synthesis and fine-grained sketch-based image retrieval (FG-SBIR). A fundamental challe…
SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
Peng Xu, Yongye Huang, Tongtong Yuan +6
We propose a deep hashing framework for sketch retrieval that, for the first time, works on a multi-million scale human sketch dataset. Leveraging on this large dataset, we explore…
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool
Feng Liu, Tao Xiang, Timothy M. Hospedales +2
In recent years, visual question answering (VQA) has become topical. The premise of VQA's significance as a benchmark in AI, is that both the image and textual question need to be…