Search Results for author: Timothy M. Hospedales

Found 74 papers, 25 papers with code

Better Practices for Domain Adaptation

no code implementations7 Sep 2023 Linus Ericsson, Da Li, Timothy M. Hospedales

However, the domain shift scenario raises a second more subtle challenge: the difficulty of performing hyperparameter optimisation (HPO) for these adaptation algorithms without access to a labelled validation set.

Benchmarking Source-Free Domain Adaptation +1

Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics

1 code implementation25 Mar 2023 Leonardo Iurada, Silvia Bucci, Timothy M. Hospedales, Tatiana Tommasi

Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life.

Domain Adaptation Fairness

Accelerating Self-Supervised Learning via Efficient Training Strategies

no code implementations11 Dec 2022 Mustafa Taha Koçyiğit, Timothy M. Hospedales, Hakan Bilen

Recently the focus of the computer vision community has shifted from expensive supervised learning towards self-supervised learning of visual representations.

Self-Supervised Learning

Fisher SAM: Information Geometry and Sharpness Aware Minimisation

no code implementations10 Jun 2022 Minyoung Kim, Da Li, Shell Xu Hu, Timothy M. Hospedales

Recent sharpness-aware minimisation (SAM) is known to find flat minima which is beneficial for better generalisation with improved robustness.

Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

1 code implementation CVPR 2022 Shell Xu Hu, Da Li, Jan Stühmer, Minyoung Kim, Timothy M. Hospedales

To this end, we explore few-shot learning from the perspective of neural network architecture, as well as a three stage pipeline of network updates under different data supplies, where unsupervised external data is considered for pre-training, base categories are used to simulate few-shot tasks for meta-training, and the scarcely labelled data of an novel task is taken for fine-tuning.

Few-Shot Image Classification Few-Shot Learning +1

Meta Mirror Descent: Optimiser Learning for Fast Convergence

no code implementations5 Mar 2022 Boyan Gao, Henry Gouk, Hae Beom Lee, Timothy M. Hospedales

The resulting framework, termed Meta Mirror Descent (MetaMD), learns to accelerate optimisation speed.


Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks

1 code implementation22 Nov 2021 Linus Ericsson, Henry Gouk, Timothy M. Hospedales

We show that learned invariances strongly affect downstream task performance and confirm that different downstream tasks benefit from polar opposite (in)variances, leading to performance loss when the standard augmentation strategy is used.

Data Augmentation Representation Learning +1

Self-Supervised Representation Learning: Introduction, Advances and Challenges

no code implementations18 Oct 2021 Linus Ericsson, Henry Gouk, Chen Change Loy, Timothy M. Hospedales

Self-supervised representation learning methods aim to provide powerful deep feature learning without the requirement of large annotated datasets, thus alleviating the annotation bottleneck that is one of the main barriers to practical deployment of deep learning today.

Representation Learning

Towards Unsupervised Sketch-based Image Retrieval

no code implementations18 May 2021 Conghui Hu, Yongxin Yang, Yunpeng Li, Timothy M. Hospedales, Yi-Zhe Song

The practical value of existing supervised sketch-based image retrieval (SBIR) algorithms is largely limited by the requirement for intensive data collection and labeling.

Representation Learning Retrieval +1

Searching for Robustness: Loss Learning for Noisy Classification Tasks

no code implementations ICCV 2021 Boyan Gao, Henry Gouk, Timothy M. Hospedales

We present a "learning to learn" approach for automatically constructing white-box classification loss functions that are robust to label noise in the training data.

Classification General Classification

FedH2L: Federated Learning with Model and Statistical Heterogeneity

no code implementations27 Jan 2021 Yiying Li, Wei Zhou, Huaimin Wang, Haibo Mi, Timothy M. Hospedales

Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy.

Federated Learning

A Simple Feature Augmentation for Domain Generalization

no code implementations ICCV 2021 Pan Li, Da Li, Wei Li, Shaogang Gong, Yanwei Fu, Timothy M. Hospedales

The topical domain generalization (DG) problem asks trained models to perform well on an unseen target domain with different data statistics from the source training domains.

Data Augmentation Domain Generalization

How Well Do Self-Supervised Models Transfer?

1 code implementation CVPR 2021 Linus Ericsson, Henry Gouk, Timothy M. Hospedales

We evaluate the transfer performance of 13 top self-supervised models on 40 downstream tasks, including many-shot and few-shot recognition, object detection, and dense prediction.

Classifier calibration Few-Shot Learning +7

On Learning Semantic Representations for Million-Scale Free-Hand Sketches

1 code implementation7 Jul 2020 Peng Xu, Yongye Huang, Tongtong Yuan, Tao Xiang, Timothy M. Hospedales, Yi-Zhe Song, Liang Wang

Specifically, we use our dual-branch architecture as a universal representation framework to design two sketch-specific deep models: (i) We propose a deep hashing model for sketch retrieval, where a novel hashing loss is specifically designed to accommodate both the abstract and messy traits of sketches.

Deep Hashing Learning Semantic Representations +1

Don't Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights

no code implementations22 Jun 2020 Linus Ericsson, Henry Gouk, Timothy M. Hospedales

We show that by learning Bayesian instance weights for the unlabelled data, we can improve the downstream classification accuracy by prioritising the most useful instances.

Meta-Learning Self-Supervised Learning

Unlimited Resolution Image Generation with R2D2-GANs

no code implementations2 Mar 2020 Marija Jegorova, Antti Ilari Karjalainen, Jose Vazquez, Timothy M. Hospedales

In this paper we present a novel simulation technique for generating high quality images of any predefined resolution.

Image Generation

Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval

1 code implementation24 Feb 2020 Ayan Kumar Bhunia, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch.

Cross-Modal Retrieval On-the-Fly Sketch Based Image Retrieval +1

Distance-Based Regularisation of Deep Networks for Fine-Tuning

1 code implementation ICLR 2021 Henry Gouk, Timothy M. Hospedales, Massimiliano Pontil

Our bound is highly relevant for fine-tuning, because providing a network with a good initialisation based on transfer learning means that learning can modify the weights less, and hence achieve tighter generalisation.

Transfer Learning

Deep Learning for Free-Hand Sketch: A Survey

2 code implementations8 Jan 2020 Peng Xu, Timothy M. Hospedales, Qiyue Yin, Yi-Zhe Song, Tao Xiang, Liang Wang

Free-hand sketches are highly illustrative, and have been widely used by humans to depict objects or stories from ancient times to the present.

Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach

no code implementations12 Nov 2019 Yu Zheng, Bowei Chen, Timothy M. Hospedales, Yongxin Yang

Compared with the benchmarked models, our model has the lowest tracking error, across a range of portfolio sizes.

Evolutionary Algorithms

Deep clustering with concrete k-means

no code implementations17 Oct 2019 Boyan Gao, Yongxin Yang, Henry Gouk, Timothy M. Hospedales

We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential of deep k-means to outperform traditional two-step feature extraction and shallow-clustering strategies.

Clustering Deep Clustering

Goal-Driven Sequential Data Abstraction

no code implementations ICCV 2019 Umar Riaz Muhammad, Yongxin Yang, Timothy M. Hospedales, Tao Xiang, Yi-Zhe Song

In the former one asks whether a machine can `understand' enough about the meaning of input data to produce a meaningful but more compact abstraction.

Benchmarking General Reinforcement Learning +2

Measuring the Transferability of Adversarial Examples

no code implementations14 Jul 2019 Deyan Petrov, Timothy M. Hospedales

Adversarial examples are of wide concern due to their impact on the reliability of contemporary machine learning systems.


Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice

1 code implementation9 May 2019 Jieru Jia, Qiuqi Ruan, Timothy M. Hospedales

Specifically, we observe that the domain discrepancy in \reid{} is due to style and content variance across datasets and demonstrate appropriate Instance and Feature Normalization alleviates much of the resulting domain-shift in Deep \reid{} models.

Person Re-Identification Test

Feature-Critic Networks for Heterogeneous Domain Generalization

2 code implementations31 Jan 2019 Yiying Li, Yongxin Yang, Wei Zhou, Timothy M. Hospedales

The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training.

Domain Generalization

Episodic Training for Domain Generalization

2 code implementations ICCV 2019 Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, Timothy M. Hospedales

In this paper, we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime.

Domain Generalization

Disjoint Label Space Transfer Learning with Common Factorised Space

no code implementations6 Dec 2018 Xiaobin Chang, Yongxin Yang, Tao Xiang, Timothy M. Hospedales

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.

Transfer Learning Unsupervised Domain Adaptation

RelationNet2: Deep Comparison Columns for Few-Shot Learning

2 code implementations17 Nov 2018 Xueting Zhang, Yu-ting Qiang, Flood Sung, Yongxin Yang, Timothy M. Hospedales

We thus propose a new deep comparison network comprised of embedding and relation modules that learn multiple non-linear distance metrics based on different levels of features simultaneously.

Few-Shot Image Classification Few-Shot Learning +1

Diversity and Sparsity: A New Perspective on Index Tracking

no code implementations6 Sep 2018 Yu Zheng, Timothy M. Hospedales, Yongxin Yang

We introduce the first index tracking method that explicitly optimises both diversity and sparsity in a single joint framework.

Hypernetwork Knowledge Graph Embeddings

1 code implementation21 Aug 2018 Ivana Balažević, Carl Allen, Timothy M. Hospedales

Knowledge graphs are graphical representations of large databases of facts, which typically suffer from incompleteness.

Knowledge Graph Embeddings Knowledge Graphs +1

Deep Factorised Inverse-Sketching

no code implementations ECCV 2018 Kaiyue Pang, Da Li, Jifei Song, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales

Instead there is a fundamental process of abstraction and iconic rendering, where overall geometry is warped and salient details are selectively included.

Retrieval Sketch-Based Image Retrieval +1

Deep Neural Decision Trees

5 code implementations19 Jun 2018 Yongxin Yang, Irene Garcia Morillo, Timothy M. Hospedales

In this work, we present Deep Neural Decision Trees (DNDT) -- tree models realised by neural networks.

Learning Deep Sketch Abstraction

no code implementations CVPR 2018 Umar Riaz Muhammad, Yongxin Yang, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales

Human free-hand sketches have been studied in various contexts including sketch recognition, synthesis and fine-grained sketch-based image retrieval (FG-SBIR).

Retrieval Sketch-Based Image Retrieval +1

SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval

1 code implementation CVPR 2018 Peng Xu, Yongye Huang, Tongtong Yuan, Kaiyue Pang, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, Zhanyu Ma, Jun Guo

Key to our network design is the embedding of unique characteristics of human sketch, where (i) a two-branch CNN-RNN architecture is adapted to explore the temporal ordering of strokes, and (ii) a novel hashing loss is specifically designed to accommodate both the temporal and abstract traits of sketches.

Deep Hashing Sketch Recognition

Multi-Level Factorisation Net for Person Re-Identification

no code implementations CVPR 2018 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.

Person Re-Identification

Deep Matching Autoencoders

no code implementations16 Nov 2017 Tanmoy Mukherjee, Makoto Yamada, Timothy M. Hospedales

In this paper we introduce Deep Matching Autoencoders (DMAE), which learn a common latent space and pairing from unpaired multi-modal data.

Image Captioning Representation Learning

Learning to Compare: Relation Network for Few-Shot Learning

12 code implementations CVPR 2018 Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, Timothy M. Hospedales

Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.

Few-Shot Image Classification Few-Shot Learning +2

Deeper, Broader and Artier Domain Generalization

5 code implementations ICCV 2017 Da Li, Yongxin Yang, Yi-Zhe Song, Timothy M. Hospedales

In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning.

Domain Generalization

Weakly Supervised Image Annotation and Segmentation with Objects and Attributes

no code implementations8 Aug 2017 Zhiyuan Shi, Yongxin Yang, Timothy M. Hospedales, Tao Xiang

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.

object-detection Object Detection +3

Scalable and Effective Deep CCA via Soft Decorrelation

no code implementations CVPR 2018 Xiaobin Chang, Tao Xiang, Timothy M. Hospedales

Specifically, exact decorrelation is replaced by soft decorrelation via a mini-batch based Stochastic Decorrelation Loss (SDL) to be optimised jointly with the other training objectives.


Actor-Critic Sequence Training for Image Captioning

no code implementations29 Jun 2017 Li Zhang, Flood Sung, Feng Liu, Tao Xiang, Shaogang Gong, Yongxin Yang, Timothy M. Hospedales

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 about what it is seeing.

Image Captioning reinforcement-learning +1

Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images

no code implementations19 Jun 2017 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.

Domain Adaptation Transfer Learning

Transferring a Semantic Representation for Person Re-Identification and Search

no code implementations CVPR 2015 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-invariant representation.

Person Re-Identification Person Search

Deep Mutual Learning

8 code implementations CVPR 2018 Ying Zhang, Tao Xiang, Timothy M. Hospedales, Huchuan Lu

Model distillation is an effective and widely used technique to transfer knowledge from a teacher to a student network.

Person Re-Identification

Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation

no code implementations9 May 2017 Zhiyuan Shi, Timothy M. Hospedales, Tao Xiang

(3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning.

Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives

no code implementations28 Nov 2016 Yongxin Yang, Timothy M. Hospedales

In this chapter, we propose a single framework that unifies multi-domain learning (MDL) and the related but better studied area of multi-task learning (MTL).

Domain Adaptation Multi-Task Learning +1

Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation

no code implementations26 Nov 2016 Xun Xu, Timothy M. Hospedales, Shaogang Gong

In this work, we improve the ability of ZSL to generalise across this domain shift in both model- and data-centric ways by formulating a visual-semantic mapping with better generalisation properties and a dynamic data re-weighting method to prioritise auxiliary data that are relevant to the target classes.

Action Recognition Data Augmentation +2

Gated Neural Networks for Option Pricing: Rationality by Design

1 code implementation14 Sep 2016 Yongxin Yang, Yu Zheng, Timothy M. Hospedales

We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable.

Inductive Bias valid

Trace Norm Regularised Deep Multi-Task Learning

1 code implementation13 Jun 2016 Yongxin Yang, Timothy M. Hospedales

We propose a framework for training multiple neural networks simultaneously.

Multi-Task Learning

Sketch Me That Shoe

no code implementations CVPR 2016 Qian Yu, Feng Liu, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, Chen-Change Loy

We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images.

Data Augmentation Retrieval +1

Multivariate Regression on the Grassmannian for Predicting Novel Domains

no code implementations CVPR 2016 Yongxin Yang, Timothy M. Hospedales

This allows a recognition model to be pre-calibrated for a new domain in advance (e. g., future time or view angle) without waiting for data collection and re-training.

Domain Adaptation regression +1

Weakly Supervised Learning of Objects, Attributes and their Associations

no code implementations31 Mar 2015 Zhiyuan Shi, Yongxin Yang, Timothy M. Hospedales, Tao Xiang

When humans describe images they tend to use combinations of nouns and adjectives, corresponding to objects and their associated attributes respectively.

Retrieval Weakly-supervised Learning

Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels

no code implementations25 Jan 2015 Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Jiechao Xiong, Shaogang Gong, Yizhou Wang, Yuan YAO

In this paper, we propose a more principled way to identify annotation outliers by formulating the subjective visual property prediction task as a unified robust learning to rank problem, tackling both the outlier detection and learning to rank jointly.

Learning-To-Rank Outlier Detection +1

Transductive Multi-view Zero-Shot Learning

no code implementations19 Jan 2015 Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Shaogang Gong

A projection from a low-level feature space to the semantic representation space is learned from the auxiliary dataset and is applied without adaptation to the target dataset.

Transfer Learning Zero-Shot Learning

A Unified Perspective on Multi-Domain and Multi-Task Learning

no code implementations23 Dec 2014 Yongxin Yang, Timothy M. Hospedales

In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL).

Domain Adaptation Multi-Task Learning +1

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