Search Results for author: Philip H. S. Torr

Found 157 papers, 84 papers with code

GDumb: A Simple Approach that Questions Our Progress in Continual Learning

1 code implementation ECCV 2020 Ameya Prabhu, Philip H. S. Torr, Puneet K. Dokania

We discuss a general formulation for the Continual Learning (CL) problem for classification---a learning task where a stream provides samples to a learner and the goal of the learner, depending on the samples it receives, is to continually upgrade its knowledge about the old classes and learn new ones.

Continual Learning Open Set Learning

LAVT: Language-Aware Vision Transformer for Referring Image Segmentation

1 code implementation4 Dec 2021 Zhao Yang, Jiaqi Wang, Yansong Tang, Kai Chen, Hengshuang Zhao, Philip H. S. Torr

Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image.

Referring Expression Segmentation Semantic Segmentation

Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS

no code implementations23 Nov 2021 Christian Schroeder de Witt, Yongchao Huang, Philip H. S. Torr, Martin Strohmeier

We then argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions, and introduce a temporally extended multi-agent reinforcement learning framework in which the resultant dynamics can be studied.

Continual Learning Multi-agent Reinforcement Learning

Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge

no code implementations15 Nov 2021 Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, Serge Belongie, Alan Yuille, Philip H. S. Torr, Song Bai

To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario.

Instance Segmentation Object Recognition +3

Deep Deterministic Uncertainty for Semantic Segmentation

no code implementations29 Oct 2021 Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty estimation using feature space densities, to semantic segmentation.

Semantic Segmentation

Detecting and Quantifying Malicious Activity with Simulation-based Inference

no code implementations6 Oct 2021 Andrew Gambardella, Bogdan State, Naeemullah Khan, Leo Tsourides, Philip H. S. Torr, Atılım Güneş Baydin

We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm.

Probabilistic Programming

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

1 code implementation11 Sep 2021 Shiyu Tang, Ruihao Gong, Yan Wang, Aishan Liu, Jiakai Wang, Xinyun Chen, Fengwei Yu, Xianglong Liu, Dawn Song, Alan Yuille, Philip H. S. Torr, DaCheng Tao

Thus, we propose RobustART, the first comprehensive Robustness investigation benchmark on ImageNet regarding ARchitecture design (49 human-designed off-the-shelf architectures and 1200+ networks from neural architecture search) and Training techniques (10+ techniques, e. g., data augmentation) towards diverse noises (adversarial, natural, and system noises).

Adversarial Robustness Data Augmentation +1

ANCER: Anisotropic Certification via Sample-wise Volume Maximization

1 code implementation9 Jul 2021 Francisco Eiras, Motasem Alfarra, M. Pawan Kumar, Philip H. S. Torr, Puneet K. Dokania, Bernard Ghanem, Adel Bibi

All prior art on randomized smoothing has focused on isotropic $\ell_p$ certification, which has the advantage of yielding certificates that can be easily compared among isotropic methods via $\ell_p$-norm radius.

Do Different Tracking Tasks Require Different Appearance Models?

1 code implementation NeurIPS 2021 Zhongdao Wang, Hengshuang Zhao, Ya-Li Li, Shengjin Wang, Philip H. S. Torr, Luca Bertinetto

We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.

Multi-Object Tracking Multi-Object Tracking and Segmentation +10

DeformRS: Certifying Input Deformations with Randomized Smoothing

1 code implementation2 Jul 2021 Motasem Alfarra, Adel Bibi, Naeemullah Khan, Philip H. S. Torr, Bernard Ghanem

Deep neural networks are vulnerable to input deformations in the form of vector fields of pixel displacements and to other parameterized geometric deformations e. g. translations, rotations, etc.

Learning Multimodal VAEs through Mutual Supervision

no code implementations23 Jun 2021 Tom Joy, Yuge Shi, Philip H. S. Torr, Tom Rainforth, Sebastian M. Schmon, N. Siddharth

Here we introduce a novel alternative, the MEME, that avoids such explicit combinations by repurposing semi-supervised VAEs to combine information between modalities implicitly through mutual supervision.

KL Guided Domain Adaptation

no code implementations14 Jun 2021 A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atılım Güneş Baydin

A common approach in the domain adaptation literature is to learn a representation of the input that has the same distributions over the source and the target domain.

Domain Adaptation

You Never Cluster Alone

no code implementations NeurIPS 2021 Yuming Shen, Ziyi Shen, Menghan Wang, Jie Qin, Philip H. S. Torr, Ling Shao

On one hand, with the corresponding assignment variables being the weight, a weighted aggregation along the data points implements the set representation of a cluster.

Contrastive Learning Self-Supervised Learning

Gradient Matching for Domain Generalization

2 code implementations20 Apr 2021 Yuge Shi, Jeffrey Seely, Philip H. S. Torr, N. Siddharth, Awni Hannun, Nicolas Usunier, Gabriel Synnaeve

We perform experiments on both the Wilds benchmark, which captures distribution shift in the real world, as well as datasets in DomainBed benchmark that focuses more on synthetic-to-real transfer.

Domain Generalization

Solving Inefficiency of Self-supervised Representation Learning

1 code implementation ICCV 2021 Guangrun Wang, Keze Wang, Guangcong Wang, Philip H. S. Torr, Liang Lin

In this paper, we reveal two contradictory phenomena in contrastive learning that we call under-clustering and over-clustering problems, which are major obstacles to learning efficiency.

Contrastive Learning Representation Learning +3

Cloth Interactive Transformer for Virtual Try-On

1 code implementation12 Apr 2021 Bin Ren, Hao Tang, Fanyang Meng, Runwei Ding, Ling Shao, Philip H. S. Torr, Nicu Sebe

2D image-based virtual try-on has attracted increased attention from the multimedia and computer vision communities.

Virtual Try-on

Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty

3 code implementations23 Feb 2021 Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal

Instead of using softmax entropy, we show that with appropriate inductive biases softmax neural nets trained with maximum likelihood reliably capture epistemic uncertainty through their feature-space density.

Active Learning

Shape-Tailored Deep Neural Networks

no code implementations16 Feb 2021 Naeemullah Khan, Angira Sharma, Ganesh Sundaramoorthi, Philip H. S. Torr

We stack multiple PDE layers to generalize a deep CNN to arbitrary regions, and apply it to segmentation.

Occluded Video Instance Segmentation: A Benchmark

1 code implementation2 Feb 2021 Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, Serge Belongie, Alan Yuille, Philip H. S. Torr, Song Bai

On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16. 3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario.

Instance Segmentation Semantic Segmentation +2

Scaling the Convex Barrier with Sparse Dual Algorithms

no code implementations ICLR 2021 Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H. S. Torr, M. Pawan Kumar

Tight and efficient neural network bounding is crucial to the scaling of neural network verification systems.

Multi-shot Temporal Event Localization: a Benchmark

1 code implementation CVPR 2021 Xiaolong Liu, Yao Hu, Song Bai, Fei Ding, Xiang Bai, Philip H. S. Torr

Current developments in temporal event or action localization usually target actions captured by a single camera.

Ranked #2 on Temporal Action Localization on THUMOS’14 (using extra training data)

Temporal Action Localization

GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation

2 code implementations13 Dec 2020 Xiaojuan Qi, Zhengzhe Liu, Renjie Liao, Philip H. S. Torr, Raquel Urtasun, Jiaya Jia

Note that GeoNet++ is generic and can be used in other depth/normal prediction frameworks to improve the quality of 3D reconstruction and pixel-wise accuracy of depth and surface normals.

3D Reconstruction Depth Estimation

Data Dependent Randomized Smoothing

1 code implementation8 Dec 2020 Motasem Alfarra, Adel Bibi, Philip H. S. Torr, Bernard Ghanem

In this work, we revisit Gaussian randomized smoothing and show that the variance of the Gaussian distribution can be optimized at each input so as to maximize the certification radius for the construction of the smooth classifier.

Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

5 code implementations18 Nov 2020 Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip H. S. Torr, Mingfei Sun, Shimon Whiteson

Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function.

SMAC Starcraft

Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation

no code implementations NeurIPS 2020 Bowen Li, Xiaojuan Qi, Philip H. S. Torr, Thomas Lukasiewicz

To achieve this, a new word-level discriminator is proposed, which provides the generator with fine-grained training feedback at word-level, to facilitate training a lightweight generator that has a small number of parameters, but can still correctly focus on specific visual attributes of an image, and then edit them without affecting other contents that are not described in the text.

Image Manipulation

Continual Learning in Low-rank Orthogonal Subspaces

1 code implementation NeurIPS 2020 Arslan Chaudhry, Naeemullah Khan, Puneet K. Dokania, Philip H. S. Torr

In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the continual learning experience is finished.

Continual Learning

Bipartite Graph Reasoning GANs for Person Image Generation

1 code implementation10 Aug 2020 Hao Tang, Song Bai, Philip H. S. Torr, Nicu Sebe

We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task.

 Ranked #1 on Pose Transfer on Market-1501 (PCKh metric)

Pose Transfer

XingGAN for Person Image Generation

2 code implementations ECCV 2020 Hao Tang, Song Bai, Li Zhang, Philip H. S. Torr, Nicu Sebe

We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i. e., translating the pose of a given person to a desired one.

 Ranked #1 on Pose Transfer on Market-1501 (IS metric)

Pose Transfer

WordCraft: An Environment for Benchmarking Commonsense Agents

1 code implementation ICML Workshop LaReL 2020 Minqi Jiang, Jelena Luketina, Nantas Nardelli, Pasquale Minervini, Philip H. S. Torr, Shimon Whiteson, Tim Rocktäschel

This is partly due to the lack of lightweight simulation environments that sufficiently reflect the semantics of the real world and provide knowledge sources grounded with respect to observations in an RL environment.

Knowledge Graphs Representation Learning

How benign is benign overfitting?

no code implementations8 Jul 2020 Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip H. S. Torr

We investigate two causes for adversarial vulnerability in deep neural networks: bad data and (poorly) trained models.

Adversarial Robustness Representation Learning

Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models

no code implementations ICLR 2021 Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth

Multimodal learning for generative models often refers to the learning of abstract concepts from the commonality of information in multiple modalities, such as vision and language.

Capturing Label Characteristics in VAEs

2 code implementations ICLR 2021 Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth

We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels.

Progressive Skeletonization: Trimming more fat from a network at initialization

1 code implementation ICLR 2021 Pau de Jorge, Amartya Sanyal, Harkirat S. Behl, Philip H. S. Torr, Gregory Rogez, Puneet K. Dokania

Recent studies have shown that skeletonization (pruning parameters) of networks \textit{at initialization} provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance.

A Revised Generative Evaluation of Visual Dialogue

1 code implementation20 Apr 2020 Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr

Evaluating Visual Dialogue, the task of answering a sequence of questions relating to a visual input, remains an open research challenge.

Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis

1 code implementation31 Mar 2020 Hao Tang, Xiaojuan Qi, Dan Xu, Philip H. S. Torr, Nicu Sebe

To tackle the first challenge, we propose to use the edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module.

Image Generation

Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training

no code implementations ICLR 2021 Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr, Martin Jaggi

As a result, we find across various workloads of data set, network model, and optimization algorithm that there exists a general scaling trend between batch size and number of training steps to convergence for the effect of data parallelism, and further, difficulty of training under sparsity.

Network Pruning

Cross-modal Deep Face Normals with Deactivable Skip Connections

1 code implementation CVPR 2020 Victoria Fernandez Abrevaya, Adnane Boukhayma, Philip H. S. Torr, Edmond Boyer

Core to our approach is a novel module that we call deactivable skip connections, which allows integrating both the auto-encoded and image-to-normal branches within the same architecture that can be trained end-to-end.

3D Face Reconstruction

FACMAC: Factored Multi-Agent Centralised Policy Gradients

3 code implementations NeurIPS 2021 Bei Peng, Tabish Rashid, Christian A. Schroeder de Witt, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Böhmer, Shimon Whiteson

We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.

Q-Learning SMAC +2

Holistically-Attracted Wireframe Parsing

1 code implementation CVPR 2020 Nan Xue, Tianfu Wu, Song Bai, Fu-Dong Wang, Gui-Song Xia, Liangpei Zhang, Philip H. S. Torr

For computing line segment proposals, a novel exact dual representation is proposed which exploits a parsimonious geometric reparameterization for line segments and forms a holistic 4-dimensional attraction field map for an input image.

Line Segment Detection

Lagrangian Decomposition for Neural Network Verification

2 code implementations24 Feb 2020 Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar

Both the algorithms offer three advantages: (i) they yield bounds that are provably at least as tight as previous dual algorithms relying on Lagrangian relaxations; (ii) they are based on operations analogous to forward/backward pass of neural networks layers and are therefore easily parallelizable, amenable to GPU implementation and able to take advantage of the convolutional structure of problems; and (iii) they allow for anytime stopping while still providing valid bounds.

Calibrating Deep Neural Networks using Focal Loss

2 code implementations NeurIPS 2020 Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania

To facilitate the use of focal loss in practice, we also provide a principled approach to automatically select the hyperparameter involved in the loss function.

Image-to-Image Translation with Text Guidance

no code implementations12 Feb 2020 Bowen Li, Xiaojuan Qi, Philip H. S. Torr, Thomas Lukasiewicz

The goal of this paper is to embed controllable factors, i. e., natural language descriptions, into image-to-image translation with generative adversarial networks, which allows text descriptions to determine the visual attributes of synthetic images.

Image-to-Image Translation Part-Of-Speech Tagging +2

Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation

1 code implementation3 Feb 2020 Hao Tang, Dan Xu, Yan Yan, Jason J. Corso, Philip H. S. Torr, Nicu Sebe

In the first stage, the input image and the conditional semantic guidance are fed into a cycled semantic-guided generation network to produce initial coarse results.

Image-to-Image Translation Translation

Unifying Training and Inference for Panoptic Segmentation

no code implementations CVPR 2020 Qizhu Li, Xiaojuan Qi, Philip H. S. Torr

This panoptic submodule gives rise to a novel propagation mechanism for panoptic logits and enables the network to output a coherent panoptic segmentation map for both "stuff" and "thing" classes, without any post-processing.

Panoptic Segmentation

Few-shot Action Recognition with Permutation-invariant Attention

no code implementations ECCV 2020 Hongguang Zhang, Li Zhang, Xiaojuan Qi, Hongdong Li, Philip H. S. Torr, Piotr Koniusz

Such encoded blocks are aggregated by permutation-invariant pooling to make our approach robust to varying action lengths and long-range temporal dependencies whose patterns are unlikely to repeat even in clips of the same class.

Action Recognition Few-Shot Learning +1

Few-shot Learning with Multi-scale Self-supervision

no code implementations6 Jan 2020 Hongguang Zhang, Philip H. S. Torr, Piotr Koniusz

To optimize the model, we leverage a scale selector to re-weight scale-wise representations based on their second-order features.

Deblurring Few-Shot Learning +2

Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation

2 code implementations CVPR 2020 Hao Tang, Dan Xu, Yan Yan, Philip H. S. Torr, Nicu Sebe

To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details.

Image Generation Scene Generation

Learning Regional Attraction for Line Segment Detection

no code implementations18 Dec 2019 Nan Xue, Song Bai, Fu-Dong Wang, Gui-Song Xia, Tianfu Wu, Liangpei Zhang, Philip H. S. Torr

Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice.

Line Segment Detection

Lessons from reinforcement learning for biological representations of space

no code implementations13 Dec 2019 Alex Muryy, Siddharth Narayanaswamy, Nantas Nardelli, Andrew Glennerster, Philip H. S. Torr

Neuroscientists postulate 3D representations in the brain in a variety of different coordinate frames (e. g. 'head-centred', 'hand-centred' and 'world-based').

ManiGAN: Text-Guided Image Manipulation

3 code implementations12 Dec 2019 Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr

The goal of our paper is to semantically edit parts of an image matching a given text that describes desired attributes (e. g., texture, colour, and background), while preserving other contents that are irrelevant to the text.

Image Manipulation

Transflow Learning: Repurposing Flow Models Without Retraining

no code implementations29 Nov 2019 Andrew Gambardella, Atılım Güneş Baydin, Philip H. S. Torr

It is well known that deep generative models have a rich latent space, and that it is possible to smoothly manipulate their outputs by traversing this latent space.

Bayesian Inference Style Transfer

Siam R-CNN: Visual Tracking by Re-Detection

1 code implementation CVPR 2020 Paul Voigtlaender, Jonathon Luiten, Philip H. S. Torr, Bastian Leibe

We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking.

Object Detection Semi-Supervised Video Object Segmentation +2

AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks

2 code implementations27 Nov 2019 Hao Tang, Hong Liu, Dan Xu, Philip H. S. Torr, Nicu Sebe

State-of-the-art methods in image-to-image translation are capable of learning a mapping from a source domain to a target domain with unpaired image data.

Image-to-Image Translation Translation

Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models

1 code implementation NeurIPS 2019 Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr

In this work, we characterise successful learning of such models as the fulfillment of four criteria: i) implicit latent decomposition into shared and private subspaces, ii) coherent joint generation over all modalities, iii) coherent cross-generation across individual modalities, and iv) improved model learning for individual modalities through multi-modal integration.

Mirror Descent View for Neural Network Quantization

1 code implementation18 Oct 2019 Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr, Richard Hartley, Puneet K. Dokania

Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity.

Quantization

Controllable Text-to-Image Generation

2 code implementations NeurIPS 2019 Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr

In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions.

Text-to-Image Generation

Branch and Bound for Piecewise Linear Neural Network Verification

no code implementations14 Sep 2019 Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar

We use the data sets to conduct a thorough experimental comparison of existing and new algorithms and to provide an inclusive analysis of the factors impacting the hardness of verification problems.

Dynamic Graph Message Passing Networks

no code implementations CVPR 2020 Li Zhang, Dan Xu, Anurag Arnab, Philip H. S. Torr

A fully-connected graph is beneficial for such modelling, however, its computational overhead is prohibitive.

Object Detection Scene Understanding +1

Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC

no code implementations17 Jul 2019 Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Alessio Tonioni, Thomas Joy, Luigi Di Stefano, Simon Walker, Philip H. S. Torr

Obtaining highly accurate depth from stereo images in real time has many applications across computer vision and robotics, but in some contexts, upper bounds on power consumption constrain the feasible hardware to embedded platforms such as FPGAs.

A Signal Propagation Perspective for Pruning Neural Networks at Initialization

1 code implementation ICLR 2020 Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr

Alternatively, a recent approach shows that pruning can be done at initialization prior to training, based on a saliency criterion called connection sensitivity.

Image Classification Network Pruning

Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

no code implementations ICLR 2020 Amartya Sanyal, Philip H. S. Torr, Puneet K. Dokania

Exciting new work on the generalization bounds for neural networks (NN) given by Neyshabur et al. , Bartlett et al. closely depend on two parameter-depenedent quantities: the Lipschitz constant upper-bound and the stable rank (a softer version of the rank operator).

Generalization Bounds Image Generation

Hijacking Malaria Simulators with Probabilistic Programming

no code implementations29 May 2019 Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth, Philip H. S. Torr, Yee Whye Teh, Atılım Güneş Baydin

Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria.

Epidemiology Probabilistic Programming

Straight to Shapes++: Real-time Instance Segmentation Made More Accurate

1 code implementation27 May 2019 Laurynas Miksys, Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr

The STS model can run at 35 FPS on a high-end desktop, but its accuracy is significantly worse than that of offline state-of-the-art methods.

Autonomous Driving Data Augmentation +3

Alpha MAML: Adaptive Model-Agnostic Meta-Learning

no code implementations17 May 2019 Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr

Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks.

Few-Shot Learning General Classification

GA-Net: Guided Aggregation Net for End-to-end Stereo Matching

2 code implementations CVPR 2019 Feihu Zhang, Victor Prisacariu, Ruigang Yang, Philip H. S. Torr

In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities.

Stereo Matching

Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks

no code implementations CVPR 2019 Eunwoo Kim, Chanho Ahn, Philip H. S. Torr, Songhwai Oh

To this end, we propose a novel network architecture producing multiple networks of different configurations, termed deep virtual networks (DVNs), for different tasks.

Learning to Adapt for Stereo

1 code implementation CVPR 2019 Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi Di Stefano, Thalaiyasingam Ajanthan, Philip H. S. Torr

Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment.

Autonomous Driving Stereo Depth Estimation

Domain Partitioning Network

no code implementations21 Feb 2019 Botos Csaba, Adnane Boukhayma, Viveka Kulharia, András Horváth, Philip H. S. Torr

Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game.

3D Hand Shape and Pose from Images in the Wild

2 code implementations CVPR 2019 Adnane Boukhayma, Rodrigo de Bem, Philip H. S. Torr

We present in this work the first end-to-end deep learning based method that predicts both 3D hand shape and pose from RGB images in the wild.

Pose Prediction

Adversarial Metric Attack and Defense for Person Re-identification

1 code implementation30 Jan 2019 Song Bai, Yingwei Li, Yuyin Zhou, Qizhu Li, Philip H. S. Torr

However, our work observes the extreme vulnerability of existing distance metrics to adversarial examples, generated by simply adding human-imperceptible perturbations to person images.

Adversarial Attack General Classification +1

Hypergraph Convolution and Hypergraph Attention

1 code implementation23 Jan 2019 Song Bai, Feihu Zhang, Philip H. S. Torr

To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i. e., hypergraph convolution and hypergraph attention.

Node Classification Representation Learning

Learn to Interpret Atari Agents

1 code implementation29 Dec 2018 Zhao Yang, Song Bai, Li Zhang, Philip H. S. Torr

In contrast to previous a-posteriori methods of visualizing DeepRL policies, we propose an end-to-end trainable framework based on Rainbow, a representative Deep Q-Network (DQN) agent.

Decision Making

Visual Dialogue without Vision or Dialogue

2 code implementations16 Dec 2018 Daniela Massiceti, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr

We characterise some of the quirks and shortcomings in the exploration of Visual Dialogue - a sequential question-answering task where the questions and corresponding answers are related through given visual stimuli.

Question Answering Visual Dialog

Fast Online Object Tracking and Segmentation: A Unifying Approach

3 code implementations CVPR 2019 Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H. S. Torr

In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.

Real-Time Visual Tracking Semi-Supervised Semantic Segmentation +2

Proximal Mean-field for Neural Network Quantization

1 code implementation ICCV 2019 Thalaiyasingam Ajanthan, Puneet K. Dokania, Richard Hartley, Philip H. S. Torr

Compressing large Neural Networks (NN) by quantizing the parameters, while maintaining the performance is highly desirable due to reduced memory and time complexity.

Image Classification Quantization

Deeper Interpretability of Deep Networks

no code implementations19 Nov 2018 Tian Xu, Jiayu Zhan, Oliver G. B. Garrod, Philip H. S. Torr, Song-Chun Zhu, Robin A. A. Ince, Philippe G. Schyns

However, understanding the information represented and processed in CNNs remains in most cases challenging.

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

1 code implementation29 Oct 2018 Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Victor A. Prisacariu, Luigi Di Stefano, Philip H. S. Torr

The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time.

Pose Estimation

SNIP: Single-shot Network Pruning based on Connection Sensitivity

6 code implementations ICLR 2019 Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr

To achieve this, we introduce a saliency criterion based on connection sensitivity that identifies structurally important connections in the network for the given task.

Image Classification Network Pruning +1

Weakly- and Semi-Supervised Panoptic Segmentation

1 code implementation ECCV 2018 Qizhu Li, Anurag Arnab, Philip H. S. Torr

We present a weakly supervised model that jointly performs both semantic- and instance-segmentation -- a particularly relevant problem given the substantial cost of obtaining pixel-perfect annotation for these tasks.

Instance Segmentation Panoptic Segmentation +3

With Friends Like These, Who Needs Adversaries?

1 code implementation NeurIPS 2018 Saumya Jetley, Nicholas A. Lord, Philip H. S. Torr

Via a novel experimental analysis, we illustrate some facts about deep convolutional networks for image classification that shed new light on their behaviour and how it connects to the problem of adversaries.

Adversarial Attack General Classification +1

Value Propagation Networks

no code implementations ICLR 2018 Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H. S. Torr, Nicolas Usunier

We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments.

Starcraft

Meta-learning with differentiable closed-form solvers

4 code implementations ICLR 2019 Luca Bertinetto, João F. Henriques, Philip H. S. Torr, Andrea Vedaldi

The main idea is to teach a deep network to use standard machine learning tools, such as ridge regression, as part of its own internal model, enabling it to quickly adapt to novel data.

Few-Shot Learning

Robustness via Deep Low-Rank Representations

no code implementations ICLR 2019 Amartya Sanyal, Varun Kanade, Philip H. S. Torr, Puneet K. Dokania

To achieve low dimensionality of learned representations, we propose an easy-to-use, end-to-end trainable, low-rank regularizer (LR) that can be applied to any intermediate layer representation of a DNN.

General Classification Image Classification +1

Learn To Pay Attention

4 code implementations ICLR 2018 Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr

We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.

Adversarial Attack General Classification +3

Three Birds One Stone: A General Architecture for Salient Object Segmentation, Edge Detection and Skeleton Extraction

no code implementations27 Mar 2018 Qibin Hou, Jiang-Jiang Liu, Ming-Ming Cheng, Ali Borji, Philip H. S. Torr

Although these tasks are inherently very different, we show that our unified approach performs very well on all of them and works far better than current single-purpose state-of-the-art methods.

Edge Detection Semantic Segmentation

WebSeg: Learning Semantic Segmentation from Web Searches

no code implementations27 Mar 2018 Qibin Hou, Ming-Ming Cheng, Jiang-Jiang Liu, Philip H. S. Torr

In this paper, we improve semantic segmentation by automatically learning from Flickr images associated with a particular keyword, without relying on any explicit user annotations, thus substantially alleviating the dependence on accurate annotations when compared to previous weakly supervised methods.

Semantic Segmentation

Real-Time Dense Stereo Matching With ELAS on FPGA Accelerated Embedded Devices

1 code implementation20 Feb 2018 Oscar Rahnama, Duncan Frost, Ondrej Miksik, Philip H. S. Torr

For many applications in low-power real-time robotics, stereo cameras are the sensors of choice for depth perception as they are typically cheaper and more versatile than their active counterparts.

Stereo Matching Stereo Matching Hand

Devon: Deformable Volume Network for Learning Optical Flow

no code implementations20 Feb 2018 Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr

State-of-the-art neural network models estimate large displacement optical flow in multi-resolution and use warping to propagate the estimation between two resolutions.

Optical Flow Estimation

FlipDial: A Generative Model for Two-Way Visual Dialogue

no code implementations CVPR 2018 Daniela Massiceti, N. Siddharth, Puneet K. Dokania, Philip H. S. Torr

We are the first to extend this paradigm to full two-way visual dialogue, where our model is capable of generating both questions and answers in sequence based on a visual input, for which we propose a set of novel evaluation measures and metrics.

Visual Dialog

Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

1 code implementation ECCV 2018 Arslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan, Philip H. S. Torr

We observe that, in addition to forgetting, a known issue while preserving knowledge, IL also suffers from a problem we call intransigence, inability of a model to update its knowledge.

Incremental Learning

Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation

no code implementations25 Jan 2018 Stuart Golodetz, Tommaso Cavallari, Nicholas A. Lord, Victor A. Prisacariu, David W. Murray, Philip H. S. Torr

Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases.

3D Reconstruction

Piecewise Linear Neural Networks verification: A comparative study

no code implementations ICLR 2018 Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar

Motivated by the need of accelerating progress in this very important area, we investigate the trade-offs of a number of different approaches based on Mixed Integer Programming, Satisfiability Modulo Theory, as well as a novel method based on the Branch-and-Bound framework.

On the Robustness of Semantic Segmentation Models to Adversarial Attacks

1 code implementation CVPR 2018 Anurag Arnab, Ondrej Miksik, Philip H. S. Torr

Deep Neural Networks (DNNs) have demonstrated exceptional performance on most recognition tasks such as image classification and segmentation.

General Classification Image Classification +2

Learning to Compare: Relation Network for Few-Shot Learning

9 code implementations CVPR 2018 Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, Timothy M. Hospedales

Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.

Few-Shot Image Classification Zero-Shot Learning

A Unified View of Piecewise Linear Neural Network Verification

2 code implementations NeurIPS 2018 Rudy Bunel, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar

The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models.

Holistic, Instance-Level Human Parsing

1 code implementation11 Sep 2017 Qizhu Li, Anurag Arnab, Philip H. S. Torr

We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to.

Human Detection Multi-Human Parsing

InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure

1 code implementation2 Aug 2017 Victor Adrian Prisacariu, Olaf Kähler, Stuart Golodetz, Michael Sapienza, Tommaso Cavallari, Philip H. S. Torr, David W. Murray

Representing the reconstruction volumetrically as a TSDF leads to most of the simplicity and efficiency that can be achieved with GPU implementations of these systems.

3D Reconstruction Simultaneous Localization and Mapping

Spatio-temporal Human Action Localisation and Instance Segmentation in Temporally Untrimmed Videos

no code implementations22 Jul 2017 Suman Saha, Gurkirt Singh, Michael Sapienza, Philip H. S. Torr, Fabio Cuzzolin

Current state-of-the-art human action recognition is focused on the classification of temporally trimmed videos in which only one action occurs per frame.

Action Recognition Instance Segmentation +1

Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

1 code implementation18 Jul 2017 Arslan Chaudhry, Puneet K. Dokania, Philip H. S. Torr

We propose an approach to discover class-specific pixels for the weakly-supervised semantic segmentation task.

Weakly-Supervised Semantic Segmentation

Learning Disentangled Representations with Semi-Supervised Deep Generative Models

1 code implementation NeurIPS 2017 N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank Wood, Philip H. S. Torr

We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder.

Representation Learning

DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agents

3 code implementations CVPR 2017 Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H. S. Torr, Manmohan Chandraker

DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i. e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents.

Future prediction Multi Future Trajectory Prediction +1

Multi-Agent Diverse Generative Adversarial Networks

1 code implementation CVPR 2018 Arnab Ghosh, Viveka Kulharia, Vinay Namboodiri, Philip H. S. Torr, Puneet K. Dokania

Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample.

Face Generation Image-to-Image Translation +1

Pixelwise Instance Segmentation with a Dynamically Instantiated Network

1 code implementation CVPR 2017 Anurag Arnab, Philip H. S. Torr

This subnetwork uses the initial category-level segmentation, along with cues from the output of an object detector, within an end-to-end CRF to predict instances.

Instance Segmentation Object Detection +1

Incremental Tube Construction for Human Action Detection

1 code implementation5 Apr 2017 Harkirat Singh Behl, Michael Sapienza, Gurkirt Singh, Suman Saha, Fabio Cuzzolin, Philip H. S. Torr

In this work, we introduce a real-time and online joint-labelling and association algorithm for action detection that can incrementally construct space-time action tubes on the most challenging action videos in which different action categories occur concurrently.

Action Detection Human robot interaction

On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation

no code implementations CVPR 2017 Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Luigi Di Stefano, Philip H. S. Torr

Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation.

Camera Relocalization

ROAM: a Rich Object Appearance Model with Application to Rotoscoping

no code implementations CVPR 2017 Ondrej Miksik, Juan-Manuel Pérez-Rúa, Philip H. S. Torr, Patrick Pérez

Rotoscoping, the detailed delineation of scene elements through a video shot, is a painstaking task of tremendous importance in professional post-production pipelines.

Learning to superoptimize programs - Workshop Version

no code implementations4 Dec 2016 Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli

Superoptimization requires the estimation of the best program for a given computational task.

Playing Doom with SLAM-Augmented Deep Reinforcement Learning

1 code implementation1 Dec 2016 Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N. Siddharth, Philip H. S. Torr

A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions.

Object Detection Q-Learning

Efficient Linear Programming for Dense CRFs

no code implementations CVPR 2017 Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar

To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent.

Semantic Segmentation

Straight to Shapes: Real-time Detection of Encoded Shapes

1 code implementation CVPR 2017 Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr

To achieve this, we use a denoising convolutional auto-encoder to establish an embedding space, and place the decoder after a fast end-to-end network trained to regress directly to the encoded shape vectors.

Denoising Object Detection

Inducing Interpretable Representations with Variational Autoencoders

no code implementations22 Nov 2016 N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr

We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference.

General Classification Variational Inference

Learning to superoptimize programs

no code implementations6 Nov 2016 Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli

This approach involves repeated sampling of modifications to the program from a proposal distribution, which are accepted or rejected based on whether they preserve correctness, and the improvement they achieve.

Fully-Trainable Deep Matching

1 code implementation12 Sep 2016 James Thewlis, Shuai Zheng, Philip H. S. Torr, Andrea Vedaldi

Deep Matching (DM) is a popular high-quality method for quasi-dense image matching.

Semantic Segmentation

Bottom-up Instance Segmentation using Deep Higher-Order CRFs

no code implementations8 Sep 2016 Anurag Arnab, Philip H. S. Torr

Traditional Scene Understanding problems such as Object Detection and Semantic Segmentation have made breakthroughs in recent years due to the adoption of deep learning.

Instance Segmentation Object Detection +2

Efficient Continuous Relaxations for Dense CRF

no code implementations22 Aug 2016 Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar

In contrast to the continuous relaxation-based energy minimisation algorithms used for sparse CRFs, the mean-field algorithm fails to provide strong theoretical guarantees on the quality of its solutions.

Semantic Segmentation Variational Inference

Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos

no code implementations4 Aug 2016 Suman Saha, Gurkirt Singh, Michael Sapienza, Philip H. S. Torr, Fabio Cuzzolin

In stage 2, the appearance network detections are boosted by combining them with the motion detection scores, in proportion to their respective spatial overlap.

Action Detection Motion Detection +1

Fully-Convolutional Siamese Networks for Object Tracking

6 code implementations30 Jun 2016 Luca Bertinetto, Jack Valmadre, João F. Henriques, Andrea Vedaldi, Philip H. S. Torr

The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself.

Object Detection Object Tracking

Adaptive Neural Compilation

1 code implementation NeurIPS 2016 Rudy Bunel, Alban Desmaison, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar

We show that it is possible to compile programs written in a low-level language to a differentiable representation.

Recurrent Instance Segmentation

no code implementations25 Nov 2015 Bernardino Romera-Paredes, Philip H. S. Torr

Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image.

Instance Segmentation Occlusion Handling +1

Joint Training of Generic CNN-CRF Models with Stochastic Optimization

no code implementations16 Nov 2015 Alexander Kirillov, Dmitrij Schlesinger, Shuai Zheng, Bogdan Savchynskyy, Philip H. S. Torr, Carsten Rother

We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters.

Stochastic Optimization

Sequential Optimization for Efficient High-Quality Object Proposal Generation

no code implementations14 Nov 2015 Ziming Zhang, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh Saligrama, Philip H. S. Torr

We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality.

Object Proposal Generation

Target Identity-Aware Network Flow for Online Multiple Target Tracking

no code implementations CVPR 2015 Afshin Dehghan, Yicong Tian, Philip H. S. Torr, Mubarak Shah

In this paper we show that multiple object tracking (MOT) can be formulated in a framework, where the detection and data-association are performed simultaneously.

Multiple Object Tracking Object Detection

Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications

no code implementations27 Nov 2014 Peng Wang, Chunhua Shen, Anton Van Den Hengel, Philip H. S. Torr

Two standard relaxation methods are widely used for solving general BQPs--spectral methods and semidefinite programming (SDP), each with their own advantages and disadvantages.

Scene Parsing Semantic Segmentation

A Framework for the Volumetric Integration of Depth Images

no code implementations3 Oct 2014 Victor Adrian Prisacariu, Olaf Kähler, Ming Ming Cheng, Carl Yuheng Ren, Julien Valentin, Philip H. S. Torr, Ian D. Reid, David W. Murray

Along with the framework we also provide a set of components for scalable reconstruction: two implementations of camera trackers, based on RGB data and on depth data, two representations of the 3D volumetric data, a dense volume and one based on hashes of subblocks, and an optional module for swapping subblocks in and out of the typically limited GPU memory.

3D Reconstruction

Object Proposal Generation using Two-Stage Cascade SVMs

no code implementations20 Jul 2014 Ziming Zhang, Philip H. S. Torr

Specifically, we explain our scale/aspect-ratio quantization scheme, and investigate the effects of combinations of $\ell_1$ and $\ell_2$ regularizers in cascade SVMs with/without ranking constraints in learning.

Object Proposal Generation Object Recognition +1

Dense Semantic Image Segmentation with Objects and Attributes

no code implementations CVPR 2014 Shuai Zheng, Ming-Ming Cheng, Jonathan Warrell, Paul Sturgess, Vibhav Vineet, Carsten Rother, Philip H. S. Torr

The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e. g. "I see a shiny red chair').

Semantic Segmentation

Feature sampling and partitioning for visual vocabulary generation on large action classification datasets

no code implementations29 May 2014 Michael Sapienza, Fabio Cuzzolin, Philip H. S. Torr

The recent trend in action recognition is towards larger datasets, an increasing number of action classes and larger visual vocabularies.

Action Classification Action Recognition +1

A Tiered Move-making Algorithm for General Non-submodular Pairwise Energies

no code implementations25 Mar 2014 Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr

The algorithm converges to a local minimum for any general pairwise potential, and we give a theoretical analysis of the properties of the algorithm, characterizing the situations in which we can expect good performance.

Image Denoising Image Stitching +1

Mesh Based Semantic Modelling for Indoor and Outdoor Scenes

no code implementations CVPR 2013 Julien P. C. Valentin, Sunando Sengupta, Jonathan Warrell, Ali Shahrokni, Philip H. S. Torr

We then define a CRF over this mesh, which is able to capture the consistency of geometric properties of the objects present in the scene.

Object Recognition

Efficient Minimization of Higher Order Submodular Functions using Monotonic Boolean Functions

no code implementations11 Sep 2011 Srikumar Ramalingam, Chris Russell, Lubor Ladicky, Philip H. S. Torr

E +n^4 {\log}^{O(1)} n)$ where $E$ is the time required to evaluate the function and $n$ is the number of variables \cite{Lee2015}.

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