215 citations · 531 across the 19 of their papers we have counts for
19 papers
Scene Text Recognition with Semantics
Joshua Cesare Placidi, Yishu Miao, Zixu Wang +1
Scene Text Recognition (STR) models have achieved high performance in recent years on benchmark datasets where text images are presented with minimal noise. Traditional STR recogni…
Contrastive Video-Language Learning with Fine-grained Frame Sampling
Zixu Wang, Yujie Zhong, Yishu Miao +2
Despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck in the area. Most video-lan…
Logically Consistent Adversarial Attacks for Soft Theorem Provers
Alexander Gaskell, Yishu Miao, Lucia Specia +1
Recent efforts within the AI community have yielded impressive results towards "soft theorem proving" over natural language sentences using language models. We propose a novel, gen…
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer +32
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and trainin…
Guiding Visual Question Generation
Nihir Vedd, Zixu Wang, Marek Rei +2
In traditional Visual Question Generation (VQG), most images have multiple concepts (e.g. objects and categories) for which a question could be generated, but models are trained to…
Cross-Modal Generative Augmentation for Visual Question Answering
Zixu Wang, Yishu Miao, Lucia Specia
Data augmentation has been shown to effectively improve the performance of multimodal machine learning models. This paper introduces a generative model for data augmentation by lev…
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation
Julia Ive, Andy Mingren Li, Yishu Miao +3
This paper addresses the problem of simultaneous machine translation (SiMT) by exploring two main concepts: (a) adaptive policies to learn a good trade-off between high translation…
Latent Variable Models for Visual Question Answering
Zixu Wang, Yishu Miao, Lucia Specia
Current work on Visual Question Answering (VQA) explore deterministic approaches conditioned on various types of image and question features. We posit that, in addition to image an…
Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision
Yujie Zhong, Linhai Xie, Sen Wang +2
In this paper, we teach machines to understand visuals and natural language by learning the mapping between sentences and noisy video snippets without explicit annotations. Firstly…
Selective Sensor Fusion for Neural Visual-Inertial Odometry
Changhao Chen, Stefano Rosa, Yishu Miao +4
Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input…
Learning with Stochastic Guidance for Navigation
Linhai Xie, Yishu Miao, Sen Wang +5
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of hig…
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu +3
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. Howe…
Neural Allocentric Intuitive Physics Prediction from Real Videos
Zhihua Wang, Stefano Rosa, Yishu Miao +4
Humans are able to make rich predictions about the future dynamics of physical objects from a glance. On the other hand, most existing computer vision approaches require strong ass…
Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation
Zhenghua Xu, Cheng Chen, Thomas Lukasiewicz +1
Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporate…
Discovering Discrete Latent Topics with Neural Variational Inference
Yishu Miao, Edward Grefenstette, Phil Blunsom
Topic models have been widely explored as probabilistic generative models of documents. Traditional inference methods have sought closed-form derivations for updating the models, h…
Latent Intention Dialogue Models
Tsung-Hsien Wen, Yishu Miao, Phil Blunsom +1
Developing a dialogue agent that is capable of making autonomous decisions and communicating by natural language is one of the long-term goals of machine learning research. Traditi…
Language as a Latent Variable: Discrete Generative Models for Sentence Compression
Yishu Miao, Phil Blunsom
In this work we explore deep generative models of text in which the latent representation of a document is itself drawn from a discrete language model distribution. We formulate a…
Neural Variational Inference for Text Processing
Yishu Miao, Lei Yu, Phil Blunsom
Recent advances in neural variational inference have spawned a renaissance in deep latent variable models. In this paper we introduce a generic variational inference framework for…
Bayesian Optimisation for Machine Translation
Yishu Miao, Ziyu Wang, Phil Blunsom
This paper presents novel Bayesian optimisation algorithms for minimum error rate training of statistical machine translation systems. We explore two classes of algorithms for effi…