277 citations · 1.7k across the 70 of their papers we have counts for
11 papers · 1 filter
Tensor Composition Net for Visual Relationship Prediction
Yuting Qiang, Yongxin Yang, Xueting Zhang +2
We present a novel Tensor Composition Net (TCN) to predict visual relationships in images. Visual Relationship Prediction (VRP) provides a more challenging test of image understand…
Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval
Aneeshan Sain, Ayan Kumar Bhunia, Yongxin Yang +2
Sketch as an image search query is an ideal alternative to text in capturing the fine-grained visual details. Prior successes on fine-grained sketch-based image retrieval (FG-SBIR)…
BézierSketch: A generative model for scalable vector sketches
Ayan Das, Yongxin Yang, Timothy Hospedales +2
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 proces…
Learning to Generate Novel Domains for Domain Generalization
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales +1
This paper focuses on domain generalization (DG), the task of learning from multiple source domains a model that generalizes well to unseen domains. A main challenge for DG is that…
Flexible Dataset Distillation: Learn Labels Instead of Images
Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
We study the problem of dataset distillation - creating a small set of synthetic examples capable of training a good model. In particular, we study the problem of label distillatio…
Sequential Learning for Domain Generalization
Da Li, Yongxin Yang, Yi-Zhe Song +1
In this paper we propose a sequential learning framework for Domain Generalization (DG), the problem of training a model that is robust to domain shift by design. Various DG approa…
Deep Domain-Adversarial Image Generation for Domain Generalisation
Kaiyang Zhou, Yongxin Yang, Timothy Hospedales +1
Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome thi…
Domain Adaptive Ensemble Learning
Kaiyang Zhou, Yongxin Yang, Yu Qiao +1
The problem of generalizing deep neural networks from multiple source domains to a target one is studied under two settings: When unlabeled target data is available, it is a multi-…
Online Meta-Critic Learning for Off-Policy Actor-Critic Methods
Wei Zhou, Yiying Li, Yongxin Yang +2
Off-Policy Actor-Critic (Off-PAC) methods have proven successful in a variety of continuous control tasks. Normally, the critic's action-value function is updated using temporal-di…
DADA: Differentiable Automatic Data Augmentation
Yonggang Li, Guosheng Hu, Yongtao Wang +3
Data augmentation (DA) techniques aim to increase data variability, and thus train deep networks with better generalisation. The pioneering AutoAugment automated the search for opt…
Sketch Less for More: On-the-Fly Fine-Grained Sketch Based Image Retrieval
Ayan Kumar Bhunia, Yongxin Yang, Timothy M. Hospedales +2
Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. Its widespread applicability is how…