Search Results for author: Yongxin Yang

Found 78 papers, 37 papers with code

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

1 code implementation 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.

Decoder Math

MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation

no code implementations3 Apr 2024 Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Fei Chen, Steven McDonagh, Gerasimos Lampouras, Ignacio Iacobacci, Sarah Parisot

To build MuLAn, we developed a training free pipeline which decomposes a monocular RGB image into a stack of RGBA layers comprising of background and isolated instances.

Prompt Engineering Text-to-Image Generation

Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models

1 code implementation3 Feb 2024 Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, Timothy Hospedales

Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs.

Instruction Following

SERF: Fine-Grained Interactive 3D Segmentation and Editing with Radiance Fields

no code implementations26 Dec 2023 Kaichen Zhou, Lanqing Hong, Enze Xie, Yongxin Yang, Zhenguo Li, Wei zhang

Although significant progress has been made in the field of 2D-based interactive editing, fine-grained 3D-based interactive editing remains relatively unexplored.

Interactive Segmentation Segmentation

Optimisation-Based Multi-Modal Semantic Image Editing

no code implementations28 Nov 2023 Bowen Li, Yongxin Yang, Steven McDonagh, Shifeng Zhang, Petru-Daniel Tudosiu, Sarah Parisot

Image editing affords increased control over the aesthetics and content of generated images.

OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning

1 code implementation24 May 2023 Jiazheng Li, Runcong Zhao, Yongxin Yang, Yulan He, Lin Gui

The remarkable performance of pre-trained large language models has revolutionised various natural language processing applications.

Data Augmentation Fact Checking +3

ChiroDiff: Modelling chirographic data with Diffusion Models

no code implementations7 Apr 2023 Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song

Such strictly-ordered discrete factorization however falls short of capturing key properties of chirographic data -- it fails to build holistic understanding of the temporal concept due to one-way visibility (causality).


Learning to Name Classes for Vision and Language Models

no code implementations CVPR 2023 Sarah Parisot, Yongxin Yang, Steven McDonagh

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content.

Descriptive Image Classification +4

Label-Efficient Object Detection via Region Proposal Network Pre-Training

no code implementations16 Nov 2022 Nanqing Dong, Linus Ericsson, Yongxin Yang, Ales Leonardis, Steven McDonagh

In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance.

Instance Segmentation Object +4

ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization

no code implementations17 Oct 2022 Qishi Dong, Awais Muhammad, Fengwei Zhou, Chuanlong Xie, Tianyang Hu, Yongxin Yang, Sung-Ho Bae, Zhenguo Li

We evaluate our paradigm on a diverse model zoo consisting of 35 models for various OoD tasks and demonstrate: (i) model ranking is better correlated with fine-tuning ranking than previous methods and up to 9859x faster than brute-force fine-tuning; (ii) OoD generalization after model ensemble with feature selection outperforms the state-of-the-art methods and the accuracy on most challenging task DomainNet is improved from 46. 5\% to 50. 6\%.

feature selection Out-of-Distribution Generalization

MEDFAIR: Benchmarking Fairness for Medical Imaging

1 code implementation4 Oct 2022 Yongshuo Zong, Yongxin Yang, Timothy Hospedales

In this work, we introduce MEDFAIR, a framework to benchmark the fairness of machine learning models for medical imaging.

Benchmarking Fairness +2

Fine-Grained VR Sketching: Dataset and Insights

1 code implementation20 Sep 2022 Ling Luo, Yulia Gryaditskaya, Yongxin Yang, Tao Xiang, Yi-Zhe Song

We then, for the first time, study the scenario of fine-grained 3D VR sketch to 3D shape retrieval, as a novel VR sketching application and a proving ground to drive out generic insights to inform future research.

3D Shape Reconstruction 3D Shape Retrieval +1

Towards 3D VR-Sketch to 3D Shape Retrieval

1 code implementation20 Sep 2022 Ling Luo, Yulia Gryaditskaya, Yongxin Yang, Tao Xiang, Yi-Zhe Song

In this paper, we offer a different perspective towards answering these questions -- we study the use of 3D sketches as an input modality and advocate a VR-scenario where retrieval is conducted.

3D Shape Retrieval Retrieval

Batch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection

no code implementations26 Jun 2022 Xiongjie Chen, Yunpeng Li, Yongxin Yang

Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications.

BIG-bench Machine Learning Out-of-Distribution Detection +2

Long-tail Recognition via Compositional Knowledge Transfer

no code implementations CVPR 2022 Sarah Parisot, Pedro M. Esperanca, Steven McDonagh, Tamas J. Madarasz, Yongxin Yang, Zhenguo Li

In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer.

Transfer Learning

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.

Attribute Domain Adaptation

SketchODE: Learning neural sketch representation in continuous time

no code implementations ICLR 2022 Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song

Learning meaningful representations for chirographic drawing data such as sketches, handwriting, and flowcharts is a gateway for understanding and emulating human creative expression.

Data Augmentation

Loss Function Learning for Domain Generalization by Implicit Gradient

no code implementations29 Sep 2021 Boyan Gao, Henry Gouk, Yongxin Yang, Timothy Hospedales

We take a different approach, and explore the impact of the ERM loss function on out-of-domain generalisation.

Domain Generalization Meta-Learning

Residual Contrastive Learning: Unsupervised Representation Learning from Residuals

no code implementations29 Sep 2021 Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh

In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks.

Contrastive Learning Image Reconstruction +3

MixStyle Neural Networks for Domain Generalization and Adaptation

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

MixStyle is easy to implement with a few lines of code, does not require modification to training objectives, and can fit a variety of learning paradigms including supervised domain generalization, semi-supervised domain generalization, and unsupervised domain adaptation.

Data Augmentation Domain Generalization +6

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 Image Reconstruction: Learning Transferable Representations from Noisy Images

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

We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs.

Contrastive Learning Demosaicking +6

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

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

The problem is especially noticeable when using modern neural networks, for which there is a significant difference between the confidence of the model and the probability of correct prediction.


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

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 Retrieval

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.

Cloud2Curve: Generation and Vectorization of Parametric Sketches

1 code implementation 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.

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.

Disentanglement Meta-Learning +2

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 Retrieval +2

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 Object

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.

Retrieval 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

Augmented Sliced Wasserstein Distances

1 code implementation ICLR 2022 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.

Computational Efficiency valid

Flexible Dataset Distillation: Learn Labels Instead of Images

2 code implementations15 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

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 +3

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

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

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

Learning Generalisable Omni-Scale Representations for Person Re-Identification

8 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.

Benchmarking General Reinforcement Learning +2

Omni-Scale Feature Learning for Person Re-Identification

16 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.

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.

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

Learning to Compare: Relation Network for Few-Shot Learning

13 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 +3

Deeper, Broader and Artier Domain Generalization

6 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.

Attribute Object +5

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

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.

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

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

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.

regression Unsupervised Domain Adaptation

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.

Attribute Object +2

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 regression +1

Sketch-a-Net that Beats Humans

2 code implementations30 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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