Search Results for author: Yishu Miao

Found 22 papers, 10 papers with code

Scene Text Recognition with Semantics

no code implementations19 Oct 2022 Joshua Cesare Placidi, Yishu Miao, Zixu Wang, Lucia Specia

Scene Text Recognition (STR) models have achieved high performance in recent years on benchmark datasets where text images are presented with minimal noise.

Scene Text Recognition

Contrastive Video-Language Learning with Fine-grained Frame Sampling

no code implementations10 Oct 2022 Zixu Wang, Yujie Zhong, Yishu Miao, Lin Ma, Lucia Specia

However, even in paired video-text segments, only a subset of the frames are semantically relevant to the corresponding text, with the remainder representing noise; where the ratio of noisy frames is higher for longer videos.

Question Answering Representation Learning +3

Logically Consistent Adversarial Attacks for Soft Theorem Provers

1 code implementation29 Apr 2022 Alexander Gaskell, Yishu Miao, Lucia Specia, Francesca Toni

We propose a novel, generative adversarial framework for probing and improving these models' reasoning capabilities.

Automated Theorem Proving

Guiding Visual Question Generation

no code implementations NAACL 2022 Nihir Vedd, Zixu Wang, Marek Rei, Yishu Miao, Lucia Specia

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 mimic an arbitrary choice of concept as given in their training data.

Question Generation Question-Generation +1

Cross-Modal Generative Augmentation for Visual Question Answering

no code implementations11 May 2021 Zixu Wang, Yishu Miao, Lucia Specia

Experiments on Visual Question Answering as downstream task demonstrate the effectiveness of the proposed generative model, which is able to improve strong UpDn-based models to achieve state-of-the-art performance.

Data Augmentation Question Answering +1

Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation

1 code implementation EACL 2021 Julia Ive, Andy Mingren Li, Yishu Miao, Ozan Caglayan, Pranava Madhyastha, Lucia Specia

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 quality and low latency; and (b) visual information to support this process by providing additional (visual) contextual information which may be available before the textual input is produced.

Machine Translation reinforcement-learning +2

Latent Variable Models for Visual Question Answering

no code implementations16 Jan 2021 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.

Benchmarking Question Answering +1

Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision

1 code implementation19 Nov 2020 Yujie Zhong, Linhai Xie, Sen Wang, Lucia Specia, Yishu Miao

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.

Retrieval Self-Supervised Learning

Selective Sensor Fusion for Neural Visual-Inertial Odometry

no code implementations CVPR 2019 Changhao Chen, Stefano Rosa, Yishu Miao, Chris Xiaoxuan Lu, Wei Wu, Andrew Markham, Niki Trigoni

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 sensory data.

Autonomous Driving Sensor Fusion

Learning with Stochastic Guidance for Navigation

1 code implementation27 Nov 2018 Linhai Xie, Yishu Miao, Sen Wang, Phil Blunsom, Zhihua Wang, Changhao Chen, Andrew Markham, Niki Trigoni

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 high variance when applied in complex real world environments.


Transferring Physical Motion Between Domains for Neural Inertial Tracking

no code implementations4 Oct 2018 Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu, Phil Blunsom, Andrew Markham, Niki Trigoni

Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent.

Domain Adaptation

Neural Allocentric Intuitive Physics Prediction from Real Videos

no code implementations7 Sep 2018 Zhihua Wang, Stefano Rosa, Yishu Miao, Zihang Lai, Linhai Xie, Andrew Markham, Niki Trigoni

In this framework, real images are first converted to a synthetic domain representation that reduces complexity arising from lighting and texture.

Memory Architectures in Recurrent Neural Network Language Models

no code implementations ICLR 2018 Dani Yogatama, Yishu Miao, Gabor Melis, Wang Ling, Adhiguna Kuncoro, Chris Dyer, Phil Blunsom

We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models.

Latent Intention Dialogue Models

1 code implementation ICML 2017 Tsung-Hsien Wen, Yishu Miao, Phil Blunsom, Steve Young

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.

reinforcement-learning Reinforcement Learning (RL) +1

Language as a Latent Variable: Discrete Generative Models for Sentence Compression

no code implementations EMNLP 2016 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.

Language Modelling Sentence Compression

Neural Variational Inference for Text Processing

6 code implementations19 Nov 2015 Yishu Miao, Lei Yu, Phil Blunsom

We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering.

Answer Selection Topic Models +1

Bayesian Optimisation for Machine Translation

no code implementations22 Dec 2014 Yishu Miao, Ziyu Wang, Phil Blunsom

This paper presents novel Bayesian optimisation algorithms for minimum error rate training of statistical machine translation systems.

Bayesian Optimisation Machine Translation +1

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