Author name cluster

Mark Johnson

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15 papers

2 author rows

IROS Conference 2022 Conference Paper

BEV-SLAM: Building a Globally-Consistent World Map Using Monocular Vision

James Ross
Oscar Mendez 0001
Avishkar Saha
Mark Johnson
Richard Bowden

The ability to produce large-scale maps for nav-igation, path planning and other tasks is a crucial step for autonomous agents, but has always been challenging. In this work, we introduce BEV-SLAM, a novel type of graph-based SLAM that aligns semantically-segmented Bird's Eye View (BEV) predictions from monocular cameras. We introduce a novel form of occlusion reasoning into BEV estimation and demonstrate its importance to aid spatial aggregation of BEV predictions. The result is a versatile SLAM system that can operate across arbitrary multi-camera configurations and can be seamlessly integrated with other sensors. We show that the use of multiple cameras significantly increases performance, and achieves lower relative error than high-performance GPS. The resulting system is able to create large, dense, globally-consistent world maps from monocular cameras mounted around an ego vehicle. The maps are metric and correctly-scaled, making them suitable for downstream navigation tasks.

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NeurIPS Conference 2021 Conference Paper

Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation

Yufei Wang
Can Xu
Huang Hu
Chongyang Tao
Stephen Wan
Mark Dras
Mark Johnson
Daxin Jiang

Sequence-to-Sequence (Seq2Seq) neural text generation models, especially the pre-trained ones (e. g. , BART and T5), have exhibited compelling performance on various natural language generation tasks. However, the black-box nature of these models limits their application in tasks where specific rules (e. g. , controllable constraints, prior knowledge) need to be executed. Previous works either design specific model structures (e. g. , Copy Mechanism corresponding to the rule "the generated output should include certain words in the source input'') or implement specialized inference algorithms (e. g. , Constrained Beam Search) to execute particular rules through the text generation. These methods require the careful design case-by-case and are difficult to support multiple rules concurrently. In this paper, we propose a novel module named Neural Rule-Execution Tracking Machine (NRETM) that can be equipped into various transformer-based generators to leverage multiple rules simultaneously to guide the neural generation model for superior generation performance in an unified and scalable way. Extensive experiments on several benchmarks verify the effectiveness of our proposed model in both controllable and general text generation tasks.

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ICRA Conference 2021 Conference Paper

There and Back Again: Self-supervised Multispectral Correspondence Estimation

Celyn Walters
Oscar Mendez 0001
Mark Johnson
Richard Bowden

Across a wide range of applications, from autonomous vehicles to medical imaging, multi-spectral images provide an opportunity to extract additional information not present in color images. One of the most important steps in making this information readily available is the accurate estimation of dense correspondences between different spectra. Due to the nature of cross-spectral images, most correspondence solving techniques for the visual domain are simply not applicable. Furthermore, most cross-spectral techniques utilize spectra-specific characteristics to perform the alignment. In this work, we aim to address the dense correspondence estimation problem in a way that generalizes to more than one spectrum. We do this by introducing a novel cycle-consistency metric that allows us to self-supervise. This, combined with our spectra-agnostic loss functions, allows us to train the same network across multiple spectra. We demonstrate our approach on the challenging task of dense RGB-FIR correspondence estimation. We also show the performance of our unmodified network on the cases of RGB-NIR and RGB-RGB, where we achieve higher accuracy than similar self-supervised approaches. Our work shows that cross-spectral correspondence estimation can be solved in a common framework that learns to generalize alignment across spectra.

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NeurIPS Conference 2018 Conference Paper

Partially-Supervised Image Captioning

Peter Anderson
Stephen Gould
Mark Johnson

Image captioning models are becoming increasingly successful at describing the content of images in restricted domains. However, if these models are to function in the wild --- for example, as assistants for people with impaired vision --- a much larger number and variety of visual concepts must be understood. To address this problem, we teach image captioning models new visual concepts from labeled images and object detection datasets. Since image labels and object classes can be interpreted as partial captions, we formulate this problem as learning from partially-specified sequence data. We then propose a novel algorithm for training sequence models, such as recurrent neural networks, on partially-specified sequences which we represent using finite state automata. In the context of image captioning, our method lifts the restriction that previously required image captioning models to be trained on paired image-sentence corpora only, or otherwise required specialized model architectures to take advantage of alternative data modalities. Applying our approach to an existing neural captioning model, we achieve state of the art results on the novel object captioning task using the COCO dataset. We further show that we can train a captioning model to describe new visual concepts from the Open Images dataset while maintaining competitive COCO evaluation scores.

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AAAI Conference 2015 Conference Paper

Topic Segmentation with an Ordering-Based Topic Model

Lan Du
John Pate
Mark Johnson

Documents from the same domain usually discuss similar topics in a similar order. However, the number of topics and the exact topics discussed in each individual document can vary. In this paper we present a simple topic model that uses generalised Mallows models and incomplete topic orderings to incorporate this ordering regularity into the probabilistic generative process of the new model. We show how to reparameterise the new model so that a point-wise sampling algorithm from the Bayesian word segmentation literature can be used for inference. This algorithm jointly samples not only the topic orders and the topic assignments but also topic segmentations of documents. Experimental results show that our model performs significantly better than the other ordering-based topic models on nearly all the corpora that we used, and competitively with other state-of-the-art topic segmentation models on corpora that have a strong ordering regularity.

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JMLR Journal 2011 Journal Article

Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning

Dorota Głowacka
John Shawe-Taylor
Alex Clark
Colin de la Higuera
Mark Johnson

Grammar induction refers to the process of learning grammars and languages from data; this finds a variety of applications in syntactic pattern recognition, the modeling of natural language acquisition, data mining and machine translation. This special topic contains several papers presenting some of recent developments in the area of grammar induction and language learning, as applied to various problems in Natural Language Processing, including supervised and unsupervised parsing and statistical machine translation. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

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JMLR Journal 2011 Journal Article

Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models

Sharon Goldwater
Thomas L. Griffiths
Mark Johnson

Standard statistical models of language fail to capture one of the most striking properties of natural languages: the power-law distribution in the frequencies of word tokens. We present a framework for developing statistical models that can generically produce power laws, breaking generative models into two stages. The first stage, the generator, can be any standard probabilistic model, while the second stage, the adaptor, transforms the word frequencies of this model to provide a closer match to natural language. We show that two commonly used Bayesian models, the Dirichlet-multinomial model and the Dirichlet process, can be viewed as special cases of our framework. We discuss two stochastic processes---the Chinese restaurant process and its two-parameter generalization based on the Pitman-Yor process---that can be used as adaptors in our framework to produce power-law distributions over word frequencies. We show that these adaptors justify common estimation procedures based on logarithmic or inverse-power transformations of empirical frequencies. In addition, taking the Pitman-Yor Chinese restaurant process as an adaptor justifies the appearance of type frequencies in formal analyses of natural language and improves the performance of a model for unsupervised learning of morphology. [abs] [ pdf ][ bib ] &copy JMLR 2011. ( edit, beta )

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NeurIPS Conference 2010 Conference Paper

Synergies in learning words and their referents

Mark Johnson
Katherine Demuth
Bevan Jones
Michael Black

This paper presents Bayesian non-parametric models that simultaneously learn to segment words from phoneme strings and learn the referents of some of those words, and shows that there is a synergistic interaction in the acquisition of these two kinds of linguistic information. The models themselves are novel kinds of Adaptor Grammars that are an extension of an embedding of topic models into PCFGs. These models simultaneously segment phoneme sequences into words and learn the relationship between non-linguistic objects to the words that refer to them. We show (i) that modelling inter-word dependencies not only improves the accuracy of the word segmentation but also of word-object relationships, and (ii) that a model that simultaneously learns word-object relationships and word segmentation segments more accurately than one that just learns word segmentation on its own. We argue that these results support an interactive view of language acquisition that can take advantage of synergies such as these.

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NeurIPS Conference 2007 Conference Paper

A Bayesian LDA-based model for semi-supervised part-of-speech tagging

Kristina Toutanova
Mark Johnson

We present a novel Bayesian model for semi-supervised part-of-speech tagging. Our model extends the Latent Dirichlet Allocation model and incorporates the intuition that words’ distributions over tags, p(t|w), are sparse. In addition we in- troduce a model for determining the set of possible tags of a word which captures important dependencies in the ambiguity classes of words. Our model outper- forms the best previously proposed model for this task on a standard dataset.

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AAAI Conference 2006 Conference Paper

A Look at Parsing and Its Applications

Matthew Lease
Mark Johnson

This paper provides a brief introduction to recent work in statistical parsing and its applications. We highlight successes to date, remaining challenges, and promising future work.

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NeurIPS Conference 2006 Conference Paper

Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models

Mark Johnson
Thomas Griffiths
Sharon Goldwater

This paper introduces adaptor grammars, a class of probabilistic models of lan- guage that generalize probabilistic context-free grammars (PCFGs). Adaptor grammars augment the probabilistic rules of PCFGs with “adaptors” that can in- duce dependencies among successive uses. With a particular choice of adaptor, based on the Pitman-Yor process, nonparametric Bayesian models of language using Dirichlet processes and hierarchical Dirichlet processes can be written as simple grammars. We present a general-purpose inference algorithm for adaptor grammars, making it easy to deﬁne and use such models, and illustrate how several existing nonparametric Bayesian models can be expressed within this framework.

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NeurIPS Conference 2005 Conference Paper

Interpolating between types and tokens by estimating power-law generators

Sharon Goldwater
Mark Johnson
Thomas Griffiths

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IJCAI Conference 2003 Conference Paper

Hierarchical Semantic Classification: Word Sense Disambiguation with World Knowledge

Massimiliano Ciaramita
Thomas Hofmann
Mark Johnson

We present a learning architecture for lexical semantic classification problems that supplements task-specific training data with background data encoding general "world knowledge". The model compiles knowledge contained in a dictionaryontology into additional training data, and integrates task-specific and background data through a novel hierarchical learning architecture. Experiments on a word sense disambiguation task provide empirical evidence that this "hierarchical classifier" outperforms a state-of-the-art standard "flat" one.

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NeurIPS Conference 2002 Conference Paper

Discriminative Learning for Label Sequences via Boosting

Yasemin Altun
Thomas Hofmann
Mark Johnson

This paper investigates a boosting approach to discriminative learning of label sequences based on a sequence rank loss function. The proposed method combines many of the advantages of boost(cid: 173) ing schemes with the efficiency of dynamic programming methods and is attractive both, conceptually and computationally. In addi(cid: 173) tion, we also discuss alternative approaches based on the Hamming loss for label sequences. The sequence boosting algorithm offers an interesting alternative to methods based on HMMs and the more recently proposed Conditional Random Fields. Applications areas for the presented technique range from natural language processing and information extraction to computational biology. We include experiments on named entity recognition and part-of-speech tag(cid: 173) ging which demonstrate the validity and competitiveness of our approach.

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IJCAI Conference 1991 Conference Paper

Logic and Feature Structures

Mark Johnson

Feature structures play an important role in linguistic knowledge representation in computational linguistics. Given the proliferation of different feature structure formalisms it is useful to have a "common language" to express them in. This paper shows how a variety of feature structures and constraints on them can be expressed in predicate logic (except for the use of circumscription for non-monotonic devices), including sorted feature values, subsumption constraints and the non-monotonic ANY values and "constraint equations". Many feature systems can be completely axiomatized in the Schonfinkel-Bernays class of first-order formulae, so the decidability of the satisfiability and validity problems for these systems follows immediately.

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