Stephen Gould

ICML Conference 2025 Conference Paper

Can We Predict Performance of Large Models across Vision-Language Tasks?

• Qinyu Zhao
• Ming Xu 0015
• Kartik Gupta
• Akshay Asthana
• Liang Zheng 0001
• Stephen Gould

Evaluating large vision-language models (LVLMs) is very expensive, due to high computational cost and the wide variety of tasks. The good news is that if we already have some observed performance scores, we may be able to infer unknown ones. In this study, we propose a new framework for predicting unknown performance scores based on observed ones from other LVLMs or tasks. We first formulate the performance prediction as a matrix completion task. Specifically, we construct a sparse performance matrix $\boldsymbol{R}$, where each entry $R_{mn}$ represents the performance score of the $m$-th model on the $n$-th dataset. By applying probabilistic matrix factorization (PMF) with Markov chain Monte Carlo (MCMC), we can complete the performance matrix, i. e. , predict unknown scores. Additionally, we estimate the uncertainty of performance prediction based on MCMC. Practitioners can evaluate their models on untested tasks with higher uncertainty first, which quickly reduces the prediction errors. We further introduce several improvements to enhance PMF for scenarios with sparse observed performance scores. Our experiments demonstrate the accuracy of PMF in predicting unknown scores, the reliability of uncertainty estimates in ordering evaluations, and the effectiveness of our enhancements for handling sparse data. Our code is available at https: //github. com/Qinyu-Allen-Zhao/CrossPred-LVLM.

NeurIPS Conference 2025 Conference Paper

Sharper Convergence Rates for Nonconvex Optimisation via Reduction Mappings

• Evan Markou
• Thalaiyasingam Ajanthan
• Stephen Gould

Many high-dimensional optimisation problems exhibit rich geometric structures in their set of minimisers, often forming smooth manifolds due to over-parametrisation or symmetries. When this structure is known, at least locally, it can be exploited through reduction mappings that reparametrise part of the parameter space to lie on the solution manifold. These reductions naturally arise from inner optimisation problems and effectively remove redundant directions, yielding a lower-dimensional objective. In this work, we introduce a general framework to understand how such reductions influence the optimisation landscape. We show that well-designed reduction mappings improve curvature properties of the objective, leading to better-conditioned problems and theoretically faster convergence for gradient-based methods. Our analysis unifies a range of scenarios where structural information at optimality is leveraged to accelerate convergence, offering a principled explanation for the empirical gains observed in such optimisation algorithms.

ICML Conference 2024 Conference Paper

An Empirical Study Into What Matters for Calibrating Vision-Language Models

• Weijie Tu
• Weijian Deng
• Dylan Campbell
• Stephen Gould
• Tom Gedeon

Vision-Language Models (VLMs) have emerged as the dominant approach for zero-shot recognition, adept at handling diverse scenarios and significant distribution changes. However, their deployment in risk-sensitive areas requires a deeper understanding of their uncertainty estimation capabilities, a relatively uncharted area. In this study, we explore the calibration properties of VLMs across different architectures, datasets, and training strategies. In particular, we analyze the uncertainty estimation performance of VLMs when calibrated in one domain, label set or hierarchy level, and tested in a different one. Our findings reveal that while VLMs are not inherently calibrated for uncertainty, temperature scaling significantly and consistently improves calibration, even across shifts in distribution and changes in label set. Moreover, VLMs can be calibrated with a very small set of examples. Through detailed experimentation, we highlight the potential applications and importance of our insights, aiming for more reliable and effective use of VLMs in critical, real-world scenarios.

TMLR Journal 2024 Journal Article

Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder

• Zheyuan Liu
• Weixuan Sun
• Damien Teney
• Stephen Gould

Composed image retrieval aims to find an image that best matches a given multi-modal user query consisting of a reference image and text pair. Existing methods commonly pre-compute image embeddings over the entire corpus and compare these to a reference image embedding modified by the query text at test time. Such a pipeline is very efficient at test time since fast vector distances can be used to evaluate candidates, but modifying the reference image embedding guided only by a short textual description can be difficult, especially independent of potential candidates. An alternative approach is to allow interactions between the query and every possible candidate, i.e., reference-text-candidate triplets, and pick the best from the entire set. Though this approach is more discriminative, for large-scale datasets the computational cost is prohibitive since pre-computation of candidate embeddings is no longer possible. We propose to combine the merits of both schemes using a two-stage model. Our first stage adopts the conventional vector distancing metric and performs a fast pruning among candidates. Meanwhile, our second stage employs a dual-encoder architecture, which effectively attends to the input triplet of reference-text-candidate and re-ranks the candidates. Both stages utilize a vision-and-language pre-trained network, which has proven beneficial for various downstream tasks. Our method consistently outperforms state-of-the-art approaches on standard benchmarks for the task. Our implementation is available at https://github.com/Cuberick-Orion/Candidate-Reranking-CIR.

NeurIPS Conference 2024 Conference Paper

Guiding Neural Collapse: Optimising Towards the Nearest Simplex Equiangular Tight Frame

• Evan Markou
• Thalaiyasingam Ajanthan
• Stephen Gould

Neural Collapse (NC) is a recently observed phenomenon in neural networks that characterises the solution space of the final classifier layer when trained until zero training loss. Specifically, NC suggests that the final classifier layer converges to a Simplex Equiangular Tight Frame (ETF), which maximally separates the weights corresponding to each class. By duality, the penultimate layer feature means also converge to the same simplex ETF. Since this simple symmetric structure is optimal, our idea is to utilise this property to improve convergence speed. Specifically, we introduce the notion of \textit{nearest simplex ETF geometry} for the penultimate layer features at any given training iteration, by formulating it as a Riemannian optimisation. Then, at each iteration, the classifier weights are implicitly set to the nearest simplex ETF by solving this inner-optimisation, which is encapsulated within a declarative node to allow backpropagation. Our experiments on synthetic and real-world architectures on classification tasks demonstrate that our approach accelerates convergence and enhances training stability.

NeurIPS Conference 2024 Conference Paper

Neural Experts: Mixture of Experts for Implicit Neural Representations

• Yizhak Ben-Shabat
• Chamin Hewa Koneputugodage
• Sameera Ramasinghe
• Stephen Gould

Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. These INRs typically learn the implicit field from sampled input points. This is often done using a single network for the entire domain, imposing many global constraints on a single function. In this paper, we propose a mixture of experts (MoE) implicit neural representation approach that enables learning local piece-wise continuous functions that simultaneously learns to subdivide the domain and fit it locally. We show that incorporating a mixture of experts architecture into existing INR formulations provides a boost in speed, accuracy, and memory requirements. Additionally, we introduce novel conditioning and pretraining methods for the gating network that improves convergence to the desired solution. We evaluate the effectiveness of our approach on multiple reconstruction tasks, including surface reconstruction, image reconstruction, and audio signal reconstruction and show improved performance compared to non-MoE methods. Code is available at our project page https: //sitzikbs. github. io/neural-experts-projectpage/.

ICAPS Conference 2024 Conference Paper

Neuro-Symbolic Learning of Lifted Action Models from Visual Traces

• Kai Xi 0001
• Stephen Gould
• Sylvie Thiébaux

Model-based planners rely on action models to describe available actions in terms of their preconditions and effects. Nonetheless, manually encoding such models is challenging, especially in complex domains. Numerous methods have been proposed to learn action models from examples of plan execution traces. However, high-level information, such as state labels within traces, is often unavailable and needs to be inferred indirectly from raw observations. In this paper, we aim to learn lifted action models from visual traces --- sequences of image-action pairs depicting discrete successive trace steps. We present ROSAME, a differentiable neuRO-Symbolic Action Model lEarner that infers action models from traces consisting of probabilistic state predictions and actions. By combining ROSAME with a deep learning computer vision model, we create an end-to-end framework that jointly learns state predictions from images and infers symbolic action models. Experimental results demonstrate that our method succeeds in both tasks, using different visual state representations, with the learned action models often matching or even surpassing those created by humans.

ICLR Conference 2024 Conference Paper

Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection

• Qinyu Zhao
• Ming Xu 0015
• Kartik Gupta
• Akshay Asthana
• Liang Zheng 0001
• Stephen Gould

Feature shaping refers to a family of methods that exhibit state-of-the-art performance for out-of-distribution (OOD) detection. These approaches manipulate the feature representation, typically from the penultimate layer of a pre-trained deep learning model, so as to better differentiate between in-distribution (ID) and OOD samples. However, existing feature-shaping methods usually employ rules manually designed for specific model architectures and OOD datasets, which consequently limit their generalization ability. To address this gap, we first formulate an abstract optimization framework for studying feature-shaping methods. We then propose a concrete reduction of the framework with a simple piecewise constant shaping function and show that existing feature-shaping methods approximate the optimal solution to the concrete optimization problem. Further, assuming that OOD data is inaccessible, we propose a formulation that yields a closed-form solution for the piecewise constant shaping function, utilizing solely the ID data. Through extensive experiments, we show that the feature-shaping function optimized by our method improves the generalization ability of OOD detection across a large variety of datasets and model architectures. Our code is available at https://github.com/Qinyu-Allen-Zhao/OptFSOOD.

ICML Conference 2023 Conference Paper

Confidence and Dispersity Speak: Characterizing Prediction Matrix for Unsupervised Accuracy Estimation

• Weijian Deng
• Yumin Suh
• Stephen Gould
• Liang Zheng 0001

This work aims to assess how well a model performs under distribution shifts without using labels. While recent methods study prediction confidence, this work reports prediction dispersity is another informative cue. Confidence reflects whether the individual prediction is certain; dispersity indicates how the overall predictions are distributed across all categories. Our key insight is that a well-performing model should give predictions with high confidence and high dispersity. That is, we need to consider both properties so as to make more accurate estimates. To this end, we use nuclear norm that has been shown to be effective in characterizing both properties. Extensive experiments validate the effectiveness of nuclear norm for various models (e. g. , ViT and ConvNeXt), different datasets (e. g. , ImageNet and CUB-200), and diverse types of distribution shifts (e. g. , style shift and reproduction shift). We show that nuclear norm is more accurate and robust in accuracy estimation than existing methods. Furthermore, we validate the feasibility of other measurements (e. g. , mutual information maximization) for characterizing dispersity and confidence. Lastly, we investigate the limitation of the nuclear norm, study its improved variant under severe class imbalance, and discuss potential directions.

ICLR Conference 2023 Conference Paper

Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths

• Ming Xu 0015
• Sourav Garg
• Michael Milford
• Stephen Gould

This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps used to solve the DTW problem. We instead propose a DTW layer based around bi-level optimisation and deep declarative networks, which we name DecDTW. By formulating DTW as a continuous, inequality constrained optimisation problem, we can compute gradients for the solution of the optimal alignment (with respect to the underlying time series) using implicit differentiation. An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW. We show that this property is particularly useful for applications where downstream loss functions are defined on the optimal alignment path itself. This naturally occurs, for instance, when learning to improve the accuracy of predicted alignments against ground truth alignments. We evaluate DecDTW on two such applications, namely the audio-to-score alignment task in music information retrieval and the visual place recognition task in robotics, demonstrating state-of-the-art results in both.

NeurIPS Conference 2023 Conference Paper

Revisiting Implicit Differentiation for Learning Problems in Optimal Control

• Ming Xu
• Timothy L. Molloy
• Stephen Gould

This paper proposes a new method for differentiating through optimal trajectories arising from non-convex, constrained discrete-time optimal control (COC) problems using the implicit function theorem (IFT). Previous works solve a differential Karush-Kuhn-Tucker (KKT) system for the trajectory derivative, and achieve this efficiently by solving an auxiliary Linear Quadratic Regulator (LQR) problem. In contrast, we directly evaluate the matrix equations which arise from applying variable elimination on the Lagrange multiplier terms in the (differential) KKT system. By appropriately accounting for the structure of the terms within the resulting equations, we show that the trajectory derivatives scale linearly with the number of timesteps. Furthermore, our approach allows for easy parallelization, significantly improved scalability with model size, direct computation of vector-Jacobian products and improved numerical stability compared to prior works. As an additional contribution, we unify prior works, addressing claims that computing trajectory derivatives using IFT scales quadratically with the number of timesteps. We evaluate our method on a both synthetic benchmark and four challenging, learning from demonstration benchmarks including a 6-DoF maneuvering quadrotor and 6-DoF rocket powered landing.

IROS Conference 2022 Conference Paper

GoferBot: A Visual Guided Human-Robot Collaborative Assembly System

• Zheyu Zhuang
• Yizhak Ben-Shabat
• Jiahao Zhang
• Stephen Gould
• Robert E. Mahony

The current transformation towards smart manufacturing has led to a growing demand for human-robot collaboration (HRC) in the manufacturing process. Perceiving and understanding the human co-worker's behaviour introduces challenges for collaborative robots to efficiently and effectively perform tasks in unstructured and dynamic environments. Integrating recent data-driven machine vision capabilities into HRC systems is a logical next step in addressing these challenges. However, in these cases, off-the-shelf components struggle due to generalisation limitations. Real-world evaluation is required in order to fully appreciate the maturity and robustness of these approaches. Furthermore, understanding the pure-vision aspects is a crucial first step before combining multiple modalities in order to understand the limitations. In this paper, we propose GoferBot, a novel vision-based semantic HRC system for a real-world assembly task. It is composed of a visual servoing module that reaches and grasps assembly parts in an unstructured multi-instance and dynamic environment, an action recognition module that performs human action prediction for implicit communication, and a visual handover module that uses the perceptual understanding of human behaviour to produce an intuitive and efficient collaborative assembly experience. GoferBot is a novel assembly system that seamlessly integrates all sub-modules by utilising implicit semantic information purely from visual perception.

NeurIPS Conference 2022 Conference Paper

On the Strong Correlation Between Model Invariance and Generalization

• Weijian Deng
• Stephen Gould
• Liang Zheng

Generalization and invariance are two essential properties of machine learning models. Generalization captures a model's ability to classify unseen data while invariance measures consistency of model predictions on transformations of the data. Existing research suggests a positive relationship: a model generalizing well should be invariant to certain visual factors. Building on this qualitative implication we make two contributions. First, we introduce effective invariance (EI), a simple and reasonable measure of model invariance which does not rely on image labels. Given predictions on a test image and its transformed version, EI measures how well the predictions agree and with what level of confidence. Second, using invariance scores computed by EI, we perform large-scale quantitative correlation studies between generalization and invariance, focusing on rotation and grayscale transformations. From a model-centric view, we observe generalization and invariance of different models exhibit a strong linear relationship, on both in-distribution and out-of-distribution datasets. From a dataset-centric view, we find a certain model's accuracy and invariance linearly correlated on different test sets. Apart from these major findings, other minor but interesting insights are also discussed.

ICLR Conference 2021 Conference Paper

Conditional Generative Modeling via Learning the Latent Space

• Sameera Ramasinghe
• Kanchana Ranasinghe
• Salman H. Khan 0001
• Nick Barnes
• Stephen Gould

Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings. We propose a novel general-purpose framework for conditional generation in multimodal spaces, that uses latent variables to model generalizable learning patterns while minimizing a family of regression cost functions. At inference, the latent variables are optimized to find solutions corresponding to multiple output modes. Compared to existing generative solutions, our approach demonstrates faster and more stable convergence, and can learn better representations for downstream tasks. Importantly, it provides a simple generic model that can perform better than highly engineered pipelines tailored using domain expertise on a variety of tasks, while generating diverse outputs. Code available at https://github.com/samgregoost/cGML.

NeurIPS Conference 2021 Conference Paper

Rethinking conditional GAN training: An approach using geometrically structured latent manifolds

• Sameera Ramasinghe
• Moshiur Farazi
• Salman H Khan
• Nick Barnes
• Stephen Gould

Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output manifolds. Although efforts have been made to improve results, they can suffer from unpleasant side-effects such as the topology mismatch between latent and output spaces. In contrast, we tackle this problem from a geometrical perspective and propose a novel training mechanism that increases both the diversity and the visual quality of a vanilla cGAN, by systematically encouraging a bi-lipschitz mapping between the latent and the output manifolds. We validate the efficacy of our solution on a baseline cGAN (i. e. , Pix2Pix) which lacks diversity, and show that by only modifying its training mechanism (i. e. , with our proposed Pix2Pix-Geo), one can achieve more diverse and realistic outputs on a broad set of image-to-image translation tasks.

ICML Conference 2021 Conference Paper

What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?

• Weijian Deng
• Stephen Gould
• Liang Zheng 0001

Understanding classifier decision under novel environments is central to the community, and a common practice is evaluating it on labeled test sets. However, in real-world testing, image annotations are difficult and expensive to obtain, especially when the test environment is changing. A natural question then arises: given a trained classifier, can we evaluate its accuracy on varying unlabeled test sets? In this work, we train semantic classification and rotation prediction in a multi-task way. On a series of datasets, we report an interesting finding, i. e. , the semantic classification accuracy exhibits a strong linear relationship with the accuracy of the rotation prediction task (Pearson’s Correlation r > 0. 88). This finding allows us to utilize linear regression to estimate classifier performance from the accuracy of rotation prediction which can be obtained on the test set through the freely generated rotation labels.

ICLR Conference 2020 Conference Paper

A Signal Propagation Perspective for Pruning Neural Networks at Initialization

• Namhoon Lee
• Thalaiyasingam Ajanthan
• Stephen Gould
• Philip H. S. Torr

Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pruning starts by training a model and then removing redundant parameters while minimizing the impact on what is learned. Alternatively, a recent approach shows that pruning can be done at initialization prior to training, based on a saliency criterion called connection sensitivity. However, it remains unclear exactly why pruning an untrained, randomly initialized neural network is effective. In this work, by noting connection sensitivity as a form of gradient, we formally characterize initialization conditions to ensure reliable connection sensitivity measurements, which in turn yields effective pruning results. Moreover, we analyze the signal propagation properties of the resulting pruned networks and introduce a simple, data-free method to improve their trainability. Our modifications to the existing pruning at initialization method lead to improved results on all tested network models for image classification tasks. Furthermore, we empirically study the effect of supervision for pruning and demonstrate that our signal propagation perspective, combined with unsupervised pruning, can be useful in various scenarios where pruning is applied to non-standard arbitrarily-designed architectures.

NeurIPS Conference 2020 Conference Paper

Language and Visual Entity Relationship Graph for Agent Navigation

• Yicong Hong
• Cristian Rodriguez
• Yuankai Qi
• Qi Wu
• Stephen Gould

Vision-and-Language Navigation (VLN) requires an agent to navigate in a real-world environment following natural language instructions. From both the textual and visual perspectives, we find that the relationships among the scene, its objects, and directional cues are essential for the agent to interpret complex instructions and correctly perceive the environment. To capture and utilize the relationships, we propose a novel Language and Visual Entity Relationship Graph for modelling the inter-modal relationships between text and vision, and the intra-modal relationships among visual entities. We propose a message passing algorithm for propagating information between language elements and visual entities in the graph, which we then combine to determine the next action to take. Experiments show that by taking advantage of the relationships we are able to improve over state-of-the-art. On the Room-to-Room (R2R) benchmark, our method achieves the new best performance on the test unseen split with success rate weighted by path length of 52%. On the Room-for-Room (R4R) dataset, our method significantly improves the previous best from 13% to 34% on the success weighted by normalized dynamic time warping.

IROS Conference 2020 Conference Paper

Spectral-GANs for High-Resolution 3D Point-cloud Generation

• Sameera Ramasinghe
• Salman H. Khan 0001
• Nick Barnes
• Stephen Gould

Point-clouds are a popular choice for robotics and computer vision tasks due to their accurate shape description and direct acquisition from range-scanners. This demands the ability to synthesize and reconstruct high-quality point-clouds. Current deep generative models for 3D data generally work on simplified representations (e. g. , voxelized objects) and cannot deal with the inherent redundancy and irregularity in point-clouds. A few recent efforts on 3D point-cloud generation offer limited resolution and their complexity grows with the increase in output resolution. In this paper, we develop a principled approach to synthesize 3D point-clouds using a spectral-domain Generative Adversarial Network (GAN). Our spectral representation is highly structured and allows us to disentangle various frequency bands such that the learning task is simplified for a GAN model. As compared to spatial-domain generative approaches, our formulation allows us to generate high-resolution point-clouds with minimal computational overhead. Furthermore, we propose a fully differentiable block to transform from the spectral to the spatial domain and back, thereby allowing us to integrate knowledge from well-established spatial models. We demonstrate that Spectral-GAN performs well for point-cloud generation task. Additionally, it can learn a highly discriminative representation in an unsupervised fashion and can be used to accurately reconstruct 3D objects. Our codes are available at https://github.com/samgregoost/Spectral-GAN/.

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.

ICML Conference 2016 Conference Paper

Learning End-to-end Video Classification with Rank-Pooling

• Basura Fernando
• Stephen Gould

We introduce a new model for representation learning and classification of video sequences. Our model is based on a convolutional neural network coupled with a novel temporal pooling layer. The temporal pooling layer relies on an inner-optimization problem to efficiently encode temporal semantics over arbitrarily long video clips into a fixed-length vector representation. Importantly, the representation and classification parameters of our model can be estimated jointly in an end-to-end manner by formulating learning as a bilevel optimization problem. Furthermore, the model can make use of any existing convolutional neural network architecture (e. g. , AlexNet or VGG) without modification or introduction of additional parameters. We demonstrate our approach on action and activity recognition tasks.

IJCAI Conference 2013 Conference Paper

Efficient Extraction and Representation of Spatial Information from Video Data

• Hajar Sadeghi Sokeh
• Stephen Gould
• Jochen Renz

Vast amounts of video data are available on the web and are being generated daily using surveillance cameras or other sources. Being able to efficiently analyse and process this data is essential for a number of different applications. We want to be able to efficiently detect activities in these videos or be able to extract and store essential information contained in these videos for future use and easy search and access. Cohn et al. (2012) proposed a comprehensive representation of spatial features that can be efficiently extracted from video and used for these purposes. In this paper, we present a modified version of this approach that is equally efficient and allows us to extract spatial information with much higher accuracy than previously possible. We present efficient algorithms both for extracting and storing spatial information from video, as well as for processing this information in order to obtain useful spatial features. We evaluate our approach and demonstrate that the extracted spatial information is considerably more accurate than that obtained from existing approaches.

JMLR Journal 2012 Journal Article

DARWIN: A Framework for Machine Learning and Computer Vision Research and Development

• Stephen Gould

We present an open-source platform-independent C++ framework for machine learning and computer vision research. The framework includes a wide range of standard machine learning and graphical models algorithms as well as reference implementations for many machine learning and computer vision applications. The framework contains Matlab wrappers for core components of the library and an experimental graphical user interface for developing and visualizing machine learning data flows. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2012. ( edit, beta )

ICML Conference 2011 Conference Paper

Max-margin Learning for Lower Linear Envelope Potentials in Binary Markov Random Fields

• Stephen Gould

ICML Conference 2010 Conference Paper

Accelerated dual decomposition for MAP inference

• Vladimir Jojic
• Stephen Gould
• Daphne Koller

ICRA Conference 2009 Conference Paper

High-accuracy 3D sensing for mobile manipulation: Improving object detection and door opening

• Morgan Quigley
• Siddharth Batra
• Stephen Gould
• Ellen Klingbeil
• Quoc V. Le
• Ashley Wellman
• Andrew Y. Ng

High-resolution 3D scanning can improve the performance of object detection and door opening, two tasks critical to the operation of mobile manipulators in cluttered homes and workplaces. We discuss how high-resolution depth information can be combined with visual imagery to improve the performance of object detection beyond what is (currently) achievable with 2D images alone, and we present door-opening and inventory-taking experiments.

NeurIPS Conference 2009 Conference Paper

Region-based Segmentation and Object Detection

• Stephen Gould
• Tianshi Gao
• Daphne Koller

Object detection and multi-class image segmentation are two closely related tasks that can be greatly improved when solved jointly by feeding information from one task to the other. However, current state-of-the-art models use a separate representation for each task making joint inference clumsy and leaving classification of many parts of the scene ambiguous. In this work, we propose a hierarchical region-based approach to joint object detection and image segmentation. Our approach reasons about pixels, regions and objects in a coherent probabilistic model. Importantly, our model gives a single unified description of the scene. We explain every pixel in the image and enforce global consistency between all variables in our model. We run experiments on challenging vision datasets and show significant improvement over state-of-the-art object detection accuracy.

NeurIPS Conference 2008 Conference Paper

Cascaded Classification Models: Combining Models for Holistic Scene Understanding

• Geremy Heitz
• Stephen Gould
• Ashutosh Saxena
• Daphne Koller

One of the original goals of computer vision was to fully understand a natural scene. This requires solving several problems simultaneously, including object detection, labeling of meaningful regions, and 3d reconstruction. While great progress has been made in tackling each of these problems in isolation, only recently have researchers again been considering the difficult task of assembling various methods to the mutual benefit of all. We consider learning a set of such classification models in such a way that they both solve their own problem and help each other. We develop a framework known as Cascaded Classification Models (CCM), where repeated instantiations of these classifiers are coupled by their input/output variables in a cascade that improves performance at each level. Our method requires only a limited “black box” interface with the models, allowing us to use very sophisticated, state-of-the-art classifiers without having to look under the hood. We demonstrate the effectiveness of our method on a large set of natural images by combining the subtasks of scene categorization, object detection, multiclass image segmentation, and 3d scene reconstruction.

NeurIPS Conference 2008 Conference Paper

Learning Bounded Treewidth Bayesian Networks

• Gal Elidan
• Stephen Gould

With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while also allowing for tractable inference. While the method of thin junction trees can, in principle, be used for this purpose, its fully greedy nature makes it prone to overfitting, particularly when data is scarce. In this work we present a novel method for learning Bayesian networks of bounded treewidth that employs global structure modifications and that is polynomial in the size of the graph and the treewidth bound. At the heart of our method is a triangulated graph that we dynamically update in a way that facilitates the addition of chain structures that increase the bound on the model's treewidth by at most one. We demonstrate the effectiveness of our ``treewidth-friendly'' method on several real-life datasets. Importantly, we also show that by using global operators, we are able to achieve better generalization even when learning Bayesian networks of unbounded treewidth.

JMLR Journal 2008 Journal Article

Learning Bounded Treewidth Bayesian Networks

• Gal Elidan
• Stephen Gould

With the increased availability of data for complex domains, it is desirable to learn Bayesian network structures that are sufficiently expressive for generalization while at the same time allow for tractable inference. While the method of thin junction trees can, in principle, be used for this purpose, its fully greedy nature makes it prone to overfitting, particularly when data is scarce. In this work we present a novel method for learning Bayesian networks of bounded treewidth that employs global structure modifications and that is polynomial both in the size of the graph and the treewidth bound. At the heart of our method is a dynamic triangulation that we update in a way that facilitates the addition of chain structures that increase the bound on the model's treewidth by at most one. We demonstrate the effectiveness of our "treewidth-friendly" method on several real-life data sets and show that it is superior to the greedy approach as soon as the bound on the treewidth is nontrivial. Importantly, we also show that by making use of global operators, we are able to achieve better generalization even when learning Bayesian networks of unbounded treewidth. [abs] [ pdf ][ bib ] &copy JMLR 2008. ( edit, beta )

UAI Conference 2008 Conference Paper

Projected Subgradient Methods for Learning Sparse Gaussians

• John C. Duchi
• Stephen Gould
• Daphne Koller

Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our approach uses the ℓ1-norm as a regularization on the inverse covariance matrix. We utilize a novel projected gradient method, which is faster than previous methods in practice and equal to the best performing of these in asymptotic complexity. We also extend the ℓ1-regularized objective to the problem of sparsifying entire blocks within the inverse covariance matrix. Our methods generalize fairly easily to this case, while other methods do not. We demonstrate that our extensions give better generalization performance on two real domains—biological network analysis and a 2D-shape modeling image task.

IJCAI Conference 2007 Conference Paper

• Stephen Gould
• Joakim Arfvidsson
• Adrian Kaehler
• Benjamin Sapp
• Marius Messner
• Gary Bradski
• Paul Baumstarck
• Sukwon Chung

Human object recognition in a physical 3-d environment is still far superior to that of any robotic vision system. We believe that one reason (out of many) for this - one that has not heretofore been significantly exploited in the artificial vision literature - is that humans use a fovea to fixate on, or near an object, thus obtaining a very high resolution image of the bject and rendering it easy to recognize. In this paper, we present a novel method for identifying and tracking objects in multi-resolution digital video of partially cluttered environments. Our method is motivated by biological vision systems and uses a learned "attentive" interest map on a low resolution data stream to direct a high resolution "fovea. " Objects that are recognized in the fovea can then be tracked using peripheral vision. Because object recognition is run only on a small foveal image, our system achieves performance in real-time object recognition and tracking that is well beyond simpler systems.