Search Results for author: Yongxin Yang

Found 61 papers, 30 papers with code

A Tree-Structured Decoder for Image-to-Markup Generation

no code implementations ICML 2020 Jianshu Zhang, Jun Du, Yongxin Yang, Yi-Zhe Song, Si Wei, Li-Rong Dai

Recent encoder-decoder approaches typically employ string decoders to convert images into serialized strings for image-to-markup.

Domain Attention Consistency for Multi-Source Domain Adaptation

1 code implementation6 Nov 2021 Zhongying Deng, Kaiyang Zhou, Yongxin Yang, Tao Xiang

Importantly, the attention module is supervised by a consistency loss, which is imposed on the distributions of channel attention weights between source and target domains.

Domain Adaptation

MixStyle Neural Networks for Domain Generalization and Adaptation

2 code implementations5 Jul 2021 Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

In this work, we address domain generalization with MixStyle, a plug-and-play, parameter-free module that is simply inserted to shallow CNN layers and requires no modification to training objectives.

Domain Generalization Object Recognition +1

EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

1 code implementation NeurIPS 2021 Ondrej Bohdal, Yongxin Yang, Timothy Hospedales

Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters.

cross-domain few-shot learning Hyperparameter Optimization

Residual Contrastive Learning for Joint Demosaicking and Denoising

no code implementations18 Jun 2021 Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh

The breakthrough of contrastive learning (CL) has fueled the recent success of self-supervised learning (SSL) in high-level vision tasks on RGB images.

Contrastive Learning Demosaicking +2

Meta-Calibration: Meta-Learning of Model Calibration Using Differentiable Expected Calibration Error

1 code implementation17 Jun 2021 Ondrej Bohdal, Yongxin Yang, Timothy Hospedales

Calibration of neural networks is a topical problem that is becoming increasingly important for real-world use of neural networks.


Domain Generalization with MixStyle

3 code implementations ICLR 2021 Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

Our method, termed MixStyle, is motivated by the observation that visual domain is closely related to image style (e. g., photo vs.~sketch images).

Domain Generalization

Pinpointing the Memory Behaviors of DNN Training

no code implementations1 Apr 2021 Jiansong Li, Xiao Dong, Guangli Li, Peng Zhao, Xueying Wang, Xiaobing Chen, Xianzhi Yu, Yongxin Yang, Zihan Jiang, Wei Cao, Lei Liu, Xiaobing Feng

The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.

StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval

no code implementations CVPR 2021 Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, Yi-Zhe Song

With this meta-learning framework, our model can not only disentangle the cross-modal shared semantic content for SBIR, but can adapt the disentanglement to any unseen user style as well, making the SBIR model truly style-agnostic.

Meta-Learning Sketch-Based Image Retrieval

Cloud2Curve: Generation and Vectorization of Parametric Sketches

no code implementations CVPR 2021 Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song

Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations.

More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval

1 code implementation CVPR 2021 Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Yongxin Yang, Tao Xiang, Yi-Zhe Song

A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) models is the data scarcity -- model performances are largely bottlenecked by the lack of sketch-photo pairs.

Cross-Modal Retrieval Semi-Supervised Sketch Based Image Retrieval +1

Context-Aware Layout to Image Generation with Enhanced Object Appearance

1 code implementation CVPR 2021 Sen He, Wentong Liao, Michael Ying Yang, Yongxin Yang, Yi-Zhe Song, Bodo Rosenhahn, Tao Xiang

We argue that these are caused by the lack of context-aware object and stuff feature encoding in their generators, and location-sensitive appearance representation in their discriminators.

Layout-to-Image Generation

Tensor Composition Net for Visual Relationship Prediction

no code implementations10 Dec 2020 Yuting Qiang, Yongxin Yang, Yanwen Guo, Timothy M. Hospedales

However Visual Relationship Prediction (VRP) also provides a more challenging test of image understanding than conventional image tagging, and is difficult to learn due to a large label-space and incomplete annotation.

Image Retrieval Multi-Label Classification +1

Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval

1 code implementation29 Jul 2020 Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, Yi-Zhe Song

In this paper, we study a further trait of sketches that has been overlooked to date, that is, they are hierarchical in terms of the levels of detail -- a person typically sketches up to various extents of detail to depict an object.

Hierarchical structure Sketch-Based Image Retrieval

BézierSketch: A generative model for scalable vector sketches

1 code implementation ECCV 2020 Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song

The study of neural generative models of human sketches is a fascinating contemporary modeling problem due to the links between sketch image generation and the human drawing process.

Image Generation

Flexible Dataset Distillation: Learn Labels Instead of Images

1 code implementation15 Jun 2020 Ondrej Bohdal, Yongxin Yang, Timothy Hospedales

In particular, we study the problem of label distillation - creating synthetic labels for a small set of real images, and show it to be more effective than the prior image-based approach to dataset distillation.

Data Summarization Meta-Learning

Augmented Sliced Wasserstein Distances

1 code implementation15 Jun 2020 Xiongjie Chen, Yongxin Yang, Yunpeng Li

While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost.

Sequential Learning for Domain Generalization

no code implementations3 Apr 2020 Da Li, Yongxin Yang, Yi-Zhe Song, Timothy Hospedales

In DG this means encountering a sequence of domains and at each step training to maximise performance on the next domain.

Domain Generalization Meta-Learning

Domain Adaptive Ensemble Learning

1 code implementation16 Mar 2020 Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang

Each such classifier is an expert to its own domain and a non-expert to others.

Domain Generalization Ensemble Learning +2

Deep Domain-Adversarial Image Generation for Domain Generalisation

no code implementations12 Mar 2020 Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, Tao Xiang

This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier.

Domain Generalization Image Generation

DADA: Differentiable Automatic Data Augmentation

1 code implementation ECCV 2020 Yonggang Li, Guosheng Hu, Yongtao Wang, Timothy Hospedales, Neil M. Robertson, Yongxin Yang

In this paper, we propose Differentiable Automatic Data Augmentation (DADA) which dramatically reduces the cost.

Data Augmentation

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

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.

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.

Deep Clustering

Learning Generalisable Omni-Scale Representations for Person Re-Identification

2 code implementations15 Oct 2019 Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, Tao Xiang

An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation.

Unsupervised Domain Adaptation Unsupervised Person Re-Identification

Simple and Effective Stochastic Neural Networks

no code implementations25 Sep 2019 Tianyuan Yu, Yongxin Yang, Da Li, Timothy Hospedales, Tao Xiang

Stochastic neural networks (SNNs) are currently topical, with several paradigms being actively investigated including dropout, Bayesian neural networks, variational information bottleneck (VIB) and noise regularized learning.

Adversarial Attack Adversarial Defense

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.

General Reinforcement Learning

Omni-Scale Feature Learning for Person Re-Identification

5 code implementations ICCV 2019 Kaiyang Zhou, Yongxin Yang, Andrea Cavallaro, Tao Xiang

As an instance-level recognition problem, person re-identification (ReID) relies on discriminative features, which not only capture different spatial scales but also encapsulate an arbitrary combination of multiple scales.

Person Re-Identification

Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction

no code implementations29 Apr 2019 Yu Zheng, Yongxin Yang, Bo-Wei Chen

This is one of the very first studies which discuss a methodological framework that incorporates prior financial domain knowledge into neural network architecture design and model training.

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

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

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

1 code implementation17 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 Metric Learning

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.

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).

Sketch-Based Image Retrieval Sketch Recognition

Learning to Compare: Relation Network for Few-Shot Learning

9 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 Zero-Shot Learning

Deeper, Broader and Artier Domain Generalization

2 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 Semantic Segmentation +1

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

Learning to Learn: Meta-Critic Networks for Sample Efficient Learning

no code implementations29 Jun 2017 Flood Sung, Li Zhang, Tao Xiang, Timothy Hospedales, Yongxin Yang

We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples.

Meta-Learning Transfer 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

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.

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

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

Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian

no code implementations28 Jul 2015 Yongxin Yang, Timothy Hospedales

Most visual recognition methods implicitly assume the data distribution remains unchanged from training to testing.

Unsupervised Domain Adaptation

When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition

no code implementations9 Apr 2015 Guosheng Hu, Yongxin Yang, Dong Yi, Josef Kittler, William Christmas, Stan Z. Li, Timothy Hospedales

In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible.

Face Recognition Metric Learning

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.

Transductive Multi-label Zero-shot Learning

no code implementations26 Mar 2015 Yanwei Fu, Yongxin Yang, Tim Hospedales, Tao Xiang, Shaogang Gong

Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems.

Multi-label zero-shot learning

Sketch-a-Net that Beats Humans

1 code implementation30 Jan 2015 Qian Yu, Yongxin Yang, Yi-Zhe Song, Tao Xiang, Timothy Hospedales

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans.

Sketch Recognition

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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