Models, code, and papers for "Philip H. S. Torr":

Pixelwise Instance Segmentation with a Dynamically Instantiated Network

Apr 07, 2017
Anurag Arnab, Philip H. S Torr

Semantic segmentation and object detection research have recently achieved rapid progress. However, the former task has no notion of different instances of the same object, and the latter operates at a coarse, bounding-box level. We propose an Instance Segmentation system that produces a segmentation map where each pixel is assigned an object class and instance identity label. Most approaches adapt object detectors to produce segments instead of boxes. In contrast, our method is based on an initial semantic segmentation module, which feeds into an instance subnetwork. 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. This part of our model is dynamically instantiated to produce a variable number of instances per image. Our end-to-end approach requires no post-processing and considers the image holistically, instead of processing independent proposals. Therefore, unlike some related work, a pixel cannot belong to multiple instances. Furthermore, far more precise segmentations are achieved, as shown by our state-of-the-art results (particularly at high IoU thresholds) on the Pascal VOC and Cityscapes datasets.

* CVPR 2017 

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Bottom-up Instance Segmentation using Deep Higher-Order CRFs

Sep 08, 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. However, the former task is not able to localise objects at a pixel level, and the latter task has no notion of different instances of objects of the same class. We focus on the task of Instance Segmentation which recognises and localises objects down to a pixel level. Our model is based on a deep neural network trained for semantic segmentation. This network incorporates a Conditional Random Field with end-to-end trainable higher order potentials based on object detector outputs. This allows us to reason about instances from an initial, category-level semantic segmentation. Our simple method effectively leverages the great progress recently made in semantic segmentation and object detection. The accurate instance-level segmentations that our network produces is reflected by the considerable improvements obtained over previous work.

* British Machine Vision Conference (BMVC) 2016 

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Object Proposal Generation using Two-Stage Cascade SVMs

Jul 20, 2014
Ziming Zhang, Philip H. S. Torr

Object proposal algorithms have shown great promise as a first step for object recognition and detection. Good object proposal generation algorithms require high object recall rate as well as low computational cost, because generating object proposals is usually utilized as a preprocessing step. The problem of how to accelerate the object proposal generation and evaluation process without decreasing recall is thus of great interest. In this paper, we propose a new object proposal generation method using two-stage cascade SVMs, where in the first stage linear filters are learned for predefined quantized scales/aspect-ratios independently, and in the second stage a global linear classifier is learned across all the quantized scales/aspect-ratios for calibration, so that all the proposals can be compared properly. The proposals with highest scores are our final output. 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. Comprehensive experiments on VOC2007 dataset are conducted, and our results achieve the state-of-the-art performance with high object recall rate and high computational efficiency. Besides, our method has been demonstrated to be suitable for not only class-specific but also generic object proposal generation.

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Recurrent Instance Segmentation

Oct 24, 2016
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. Current instance segmentation approaches consist of ensembles of modules that are trained independently of each other, thus missing opportunities for joint learning. Here we propose a new instance segmentation paradigm consisting in an end-to-end method that learns how to segment instances sequentially. The model is based on a recurrent neural network that sequentially finds objects and their segmentations one at a time. This net is provided with a spatial memory that keeps track of what pixels have been explained and allows occlusion handling. In order to train the model we designed a principled loss function that accurately represents the properties of the instance segmentation problem. In the experiments carried out, we found that our method outperforms recent approaches on multiple person segmentation, and all state of the art approaches on the Plant Phenotyping dataset for leaf counting.

* ECCV 2016. 14th European Conference on Computer Vision 
* 14 pages (main paper). 24 pages including references and appendix 

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Unifying Training and Inference for Panoptic Segmentation

Jan 14, 2020
Qizhu Li, Xiaojuan Qi, Philip H. S. Torr

We present an end-to-end network to bridge the gap between training and inference pipeline for panoptic segmentation, a task that seeks to partition an image into semantic regions for "stuff" and object instances for "things". In contrast to recent works, our network exploits a parametrised, yet lightweight panoptic segmentation submodule, powered by an end-to-end learnt dense instance affinity, to capture the probability that any pair of pixels belong to the same instance. 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. Reaping the benefits of end-to-end training, our full system sets new records on the popular street scene dataset, Cityscapes, achieving 61.4 PQ with a ResNet-50 backbone using only the fine annotations. On the challenging COCO dataset, our ResNet-50-based network also delivers state-of-the-art accuracy of 43.4 PQ. Moreover, our network flexibly works with and without object mask cues, performing competitively under both settings, which is of interest for applications with computation budgets.

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Few-shot Learning with Multi-scale Self-supervision

Jan 06, 2020
Hongguang Zhang, Philip H. S. Torr, Piotr Koniusz

Learning concepts from the limited number of datapoints is a challenging task usually addressed by the so-called one- or few-shot learning. Recently, an application of second-order pooling in few-shot learning demonstrated its superior performance due to the aggregation step handling varying image resolutions without the need of modifying CNNs to fit to specific image sizes, yet capturing highly descriptive co-occurrences. However, using a single resolution per image (even if the resolution varies across a dataset) is suboptimal as the importance of image contents varies across the coarse-to-fine levels depending on the object and its class label e. g., generic objects and scenes rely on their global appearance while fine-grained objects rely more on their localized texture patterns. Multi-scale representations are popular in image deblurring, super-resolution and image recognition but they have not been investigated in few-shot learning due to its relational nature complicating the use of standard techniques. In this paper, we propose a novel multi-scale relation network based on the properties of second-order pooling to estimate image relations in few-shot setting. To optimize the model, we leverage a scale selector to re-weight scale-wise representations based on their second-order features. Furthermore, we propose to a apply self-supervised scale prediction. Specifically, we leverage an extra discriminator to predict the scale labels and the scale discrepancy between pairs of images. Our model achieves state-of-the-art results on standard few-shot learning datasets.

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Hypergraph Convolution and Hypergraph Attention

Jan 23, 2019
Song Bai, Feihu Zhang, Philip H. S. Torr

Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation. 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. Whilst hypergraph convolution defines the basic formulation of performing convolution on a hypergraph, hypergraph attention further enhances the capacity of representation learning by leveraging an attention module. With the two operators, a graph neural network is readily extended to a more flexible model and applied to diverse applications where non-pairwise relationships are observed. Extensive experimental results with semi-supervised node classification demonstrate the effectiveness of hypergraph convolution and hypergraph attention.

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Weakly- and Semi-Supervised Panoptic Segmentation

Aug 10, 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. In contrast to many popular instance segmentation approaches based on object detectors, our method does not predict any overlapping instances. Moreover, we are able to segment both "thing" and "stuff" classes, and thus explain all the pixels in the image. "Thing" classes are weakly-supervised with bounding boxes, and "stuff" with image-level tags. We obtain state-of-the-art results on Pascal VOC, for both full and weak supervision (which achieves about 95% of fully-supervised performance). Furthermore, we present the first weakly-supervised results on Cityscapes for both semantic- and instance-segmentation. Finally, we use our weakly supervised framework to analyse the relationship between annotation quality and predictive performance, which is of interest to dataset creators.

* ECCV 2018. The first two authors contributed equally 

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With Friends Like These, Who Needs Adversaries?

Jul 23, 2018
Saumya Jetley, Nicholas A. Lord, Philip H. S. Torr

The vulnerability of deep image classification networks to adversarial attack is now well known, but less well understood. Via a novel experimental analysis, we illustrate some facts about deep convolutional networks (DCNs) that shed new light on their behaviour and its connection to the problem of adversaries, with two key results. The first is a straightforward explanation of the existence of universal adversarial perturbations and their association with specific class identities, obtained by analysing the properties of nets' logit responses as functions of 1D movements along specific image-space directions. The second is the clear demonstration of the tight coupling between classification performance and vulnerability to adversarial attack within the spaces spanned by these directions. Prior work has noted the importance of low-dimensional subspaces in adversarial vulnerability: we illustrate that this likewise represents the nets' notion of saliency. In all, we provide a digestible perspective from which to understand previously reported results which have appeared disjoint or contradictory, with implications for efforts to construct neural nets that are both accurate and robust to adversarial attack.

* 15 pages, 7 figures, 1 table 

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On the Robustness of Semantic Segmentation Models to Adversarial Attacks

Jul 08, 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. However, they have also been shown to be vulnerable to adversarial examples. This phenomenon has recently attracted a lot of attention but it has not been extensively studied on multiple, large-scale datasets and structured prediction tasks such as semantic segmentation which often require more specialised networks with additional components such as CRFs, dilated convolutions, skip-connections and multiscale processing. In this paper, we present what to our knowledge is the first rigorous evaluation of adversarial attacks on modern semantic segmentation models, using two large-scale datasets. We analyse the effect of different network architectures, model capacity and multiscale processing, and show that many observations made on the task of classification do not always transfer to this more complex task. Furthermore, we show how mean-field inference in deep structured models, multiscale processing (and more generally, input transformations) naturally implement recently proposed adversarial defenses. Our observations will aid future efforts in understanding and defending against adversarial examples. Moreover, in the shorter term, we show how to effectively benchmark robustness and show which segmentation models should currently be preferred in safety-critical applications due to their inherent robustness.

* CVPR 2018 extended version 

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Intriguing Properties of Learned Representations

Jun 11, 2018
Amartya Sanyal, Varun Kanade, Philip H. S. Torr

A key feature of neural networks, particularly deep convolutional neural networks, is their ability to "learn" useful representations from data. The very last layer of a neural network is then simply a linear model trained on these "learned" representations. Despite their numerous applications in other tasks such as classification, retrieval, clustering etc., a.k.a. transfer learning, not much work has been published that investigates the structure of these representations or indeed whether structure can be imposed on them during the training process. In this paper, we study the effective dimensionality of the learned representations by models that have proved highly successful for image classification. We focus on ResNet-18, ResNet-50 and VGG-19 and observe that when trained on CIFAR10 or CIFAR100, the learned representations exhibit a fairly low rank structure. We propose a modification to the training procedure, which further encourages low rank structure on learned activations. Empirically, we show that this has implications for robustness to adversarial examples and compression.

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Holistic, Instance-Level Human Parsing

Sep 11, 2017
Qizhu Li, Anurag Arnab, Philip H. S. Torr

Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. 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. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.

* Poster at BMVC 2017 

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

May 29, 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. State-of-the-art human action classification in challenging video data is currently based on a bag-of-visual-words pipeline in which space-time features are aggregated globally to form a histogram. The strategies chosen to sample features and construct a visual vocabulary are critical to performance, in fact often dominating performance. In this work we provide a critical evaluation of various approaches to building a vocabulary and show that good practises do have a significant impact. By subsampling and partitioning features strategically, we are able to achieve state-of-the-art results on 5 major action recognition datasets using relatively small visual vocabularies.

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

Mar 25, 2014
Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr

A large number of problems in computer vision can be modelled as energy minimization problems in a Markov Random Field (MRF) or Conditional Random Field (CRF) framework. Graph-cuts based $\alpha$-expansion is a standard move-making method to minimize the energy functions with sub-modular pairwise terms. However, certain problems require more complex pairwise terms where the $\alpha$-expansion method is generally not applicable. In this paper, we propose an iterative {\em tiered move making algorithm} which is able to handle general pairwise terms. Each move to the next configuration is based on the current labeling and an optimal tiered move, where each tiered move requires one application of the dynamic programming based tiered labeling method introduced in Felzenszwalb et. al. \cite{tiered_cvpr_felzenszwalbV10}. 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. We first evaluate our method on an object-class segmentation problem using the Pascal VOC-11 segmentation dataset where we learn general pairwise terms. Further we evaluate the algorithm on many other benchmark labeling problems such as stereo, image segmentation, image stitching and image denoising. Our method consistently gets better accuracy and energy values than alpha-expansion, loopy belief propagation (LBP), quadratic pseudo-boolean optimization (QPBO), and is competitive with TRWS.

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Transflow Learning: Repurposing Flow Models Without Retraining

Dec 05, 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. Recently, architectures have emerged that allow for more complex manipulations, such as making an image look as though it were from a different class, or painted in a certain style. These methods typically require large amounts of training in order to learn a single class of manipulations. We present Transflow Learning, a method for transforming a pre-trained generative model so that its outputs more closely resemble data that we provide afterwards. In contrast to previous methods, Transflow Learning does not require any training at all, and instead warps the probability distribution from which we sample latent vectors using Bayesian inference. Transflow Learning can be used to solve a wide variety of tasks, such as neural style transfer and few-shot classification.

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Stable Rank Normalization for Improved Generalization in Neural Networks and GANs

Jun 12, 2019
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). This leads to an interesting question of whether controlling these quantities might improve the generalization behaviour of NNs. To this end, we propose stable rank normalization (SRN), a novel, optimal, and computationally efficient weight-normalization scheme which minimizes the stable rank of a linear operator. Surprisingly we find that SRN, inspite of being non-convex problem, can be shown to have a unique optimal solution. Moreover, we show that SRN allows control of the data-dependent empirical Lipschitz constant, which in contrast to the Lipschitz upper-bound, reflects the true behaviour of a model on a given dataset. We provide thorough analyses to show that SRN, when applied to the linear layers of a NN for classification, provides striking improvements-11.3% on the generalization gap compared to the standard NN along with significant reduction in memorization. When applied to the discriminator of GANs (called SRN-GAN) it improves Inception, FID, and Neural divergence scores on the CIFAR 10/100 and CelebA datasets, while learning mappings with low empirical Lipschitz constants.

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3D Hand Shape and Pose from Images in the Wild

Feb 09, 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. Our network consists of the concatenation of a deep convolutional encoder, and a fixed model-based decoder. Given an input image, and optionally 2D joint detections obtained from an independent CNN, the encoder predicts a set of hand and view parameters. The decoder has two components: A pre-computed articulated mesh deformation hand model that generates a 3D mesh from the hand parameters, and a re-projection module controlled by the view parameters that projects the generated hand into the image domain. We show that using the shape and pose prior knowledge encoded in the hand model within a deep learning framework yields state-of-the-art performance in 3D pose prediction from images on standard benchmarks, and produces geometrically valid and plausible 3D reconstructions. Additionally, we show that training with weak supervision in the form of 2D joint annotations on datasets of images in the wild, in conjunction with full supervision in the form of 3D joint annotations on limited available datasets allows for good generalization to 3D shape and pose predictions on images in the wild.

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Meta Learning Deep Visual Words for Fast Video Object Segmentation

Dec 04, 2018
Harkirat Singh Behl, Mohammad Najafi, Philip H. S. Torr

Meta learning has attracted a lot of attention recently. In this paper, we propose a fast and novel meta learning based method for video object segmentation that quickly adapts to new domains without any fine-tuning. The proposed model performs segmentation by matching pixels to object parts. The model represents object parts using deep visual words, and meta learns them with the objective of minimizing the object segmentation loss. This is however not straightforward as no ground-truth information is available for the object parts. We tackle this problem by iteratively performing unsupervised learning of the deep visual words, followed by supervised learning of the segmentation problem, given the visual words. Our experiments show that the proposed method performs on-par with state-of-the-art methods, while being computationally much more efficient.

* The first two authors have contributed equally and assert joint first authorship 

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Discovering Class-Specific Pixels for Weakly-Supervised Semantic Segmentation

Jul 18, 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. We show that properly combining saliency and attention maps allows us to obtain reliable cues capable of significantly boosting the performance. First, we propose a simple yet powerful hierarchical approach to discover the class-agnostic salient regions, obtained using a salient object detector, which otherwise would be ignored. Second, we use fully convolutional attention maps to reliably localize the class-specific regions in a given image. We combine these two cues to discover class-specific pixels which are then used as an approximate ground truth for training a CNN. While solving the weakly supervised semantic segmentation task, we ensure that the image-level classification task is also solved in order to enforce the CNN to assign at least one pixel to each object present in the image. Experimentally, on the PASCAL VOC12 val and test sets, we obtain the mIoU of 60.8% and 61.9%, achieving the performance gains of 5.1% and 5.2% compared to the published state-of-the-art results. The code is made publicly available.

* 28th British Machine Vision Conference (BMVC), 2017 

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