Search Results for author: Philip Torr

Found 67 papers, 21 papers with code

MP2: A Momentum Contrast Approach for Recommendation with Pointwise and Pairwise Learning

no code implementations18 Apr 2022 Menghan Wang, Yuchen Guo, Zhenqi Zhao, Guangzheng Hu, Yuming Shen, Mingming Gong, Philip Torr

To alleviate the influence of the annotation bias, we perform a momentum update to ensure a consistent item representation.

Task-Agnostic Robust Representation Learning

no code implementations15 Mar 2022 A. Tuan Nguyen, Ser Nam Lim, Philip Torr

To tackle this problem, a great amount of research has been done to study the training procedure of a network to improve its robustness.

Representation Learning Self-Supervised Learning

Language Matters: A Weakly Supervised Pre-training Approach for Scene Text Detection and Spotting

no code implementations8 Mar 2022 Chuhui Xue, Yu Hao, Shijian Lu, Philip Torr, Song Bai

This paper presents a weakly supervised pre-training method that can acquire effective scene text representations by jointly learning and aligning visual and textual information.

Optical Character Recognition Scene Text Detection

Gradients without Backpropagation

1 code implementation17 Feb 2022 Atılım Güneş Baydin, Barak A. Pearlmutter, Don Syme, Frank Wood, Philip Torr

Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning.

Deeply Explain CNN via Hierarchical Decomposition

no code implementations23 Jan 2022 Ming-Ming Cheng, Peng-Tao Jiang, Ling-Hao Han, Liang Wang, Philip Torr

The proposed framework can generate a deep hierarchy of strongly associated supporting evidence for the network decision, which provides insight into the decision-making process.

Decision Making

Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning

1 code implementation9 Dec 2021 Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip Torr, Song Bai, Vincent Y. F. Tan

Specifically, we experimentally show that directly encouraging CIL Learner at the initial phase to output similar representations as the model jointly trained on all classes can greatly boost the CIL performance.

class-incremental learning Incremental Learning

A Continuous Mapping For Augmentation Design

no code implementations NeurIPS 2021 Keyu Tian, Chen Lin, Ser Nam Lim, Wanli Ouyang, Puneet Dokania, Philip Torr

Automated data augmentation (ADA) techniques have played an important role in boosting the performance of deep models.

Data Augmentation

Overcoming the Convex Barrier for Simplex Inputs

no code implementations NeurIPS 2021 Harkirat Singh Behl, M. Pawan Kumar, Philip Torr, Krishnamurthy Dvijotham

Recent progress in neural network verification has challenged the notion of a convex barrier, that is, an inherent weakness in the convex relaxation of the output of a neural network.

PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer

1 code implementation23 Nov 2021 Zitong Yu, Yuming Shen, Jingang Shi, Hengshuang Zhao, Philip Torr, Guoying Zhao

Remote photoplethysmography (rPPG), which aims at measuring heart activities and physiological signals from facial video without any contact, has great potential in many applications (e. g., remote healthcare and affective computing).

Adversarial Examples on Segmentation Models Can be Easy to Transfer

no code implementations22 Nov 2021 Jindong Gu, Hengshuang Zhao, Volker Tresp, Philip Torr

The high transferability achieved by our method shows that, in contrast to the observations in previous work, adversarial examples on a segmentation model can be easy to transfer to other segmentation models.

Adversarial Robustness Classification +3

TransMix: Attend to Mix for Vision Transformers

1 code implementation18 Nov 2021 Jie-Neng Chen, Shuyang Sun, Ju He, Philip Torr, Alan Yuille, Song Bai

The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map.

Instance Segmentation Object Detection +1

Mix-MaxEnt: Creating High Entropy Barriers To Improve Accuracy and Uncertainty Estimates of Deterministic Neural Networks

no code implementations29 Sep 2021 Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip Torr, Puneet K. Dokania

We propose an extremely simple approach to regularize a single deterministic neural network to obtain improved accuracy and reliable uncertainty estimates.

Memory-Driven Text-to-Image Generation

no code implementations29 Sep 2021 Bowen Li, Philip Torr, Thomas Lukasiewicz

We introduce a memory-driven semi-parametric approach to text-to-image generation, which is based on both parametric and non-parametric techniques.

Text to image generation Text-to-Image Generation

Towards fast and effective single-step adversarial training

no code implementations29 Sep 2021 Pau de Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip Torr, Grégory Rogez, Puneet K. Dokania

In this work, we methodically revisit the role of noise and clipping in single-step adversarial training.

Communicating via Markov Decision Processes

no code implementations29 Sep 2021 Samuel Sokota, Christian Schroeder de Witt, Maximilian Igl, Luisa M Zintgraf, Philip Torr, J Zico Kolter, Shimon Whiteson, Jakob Nicolaus Foerster

We consider the problem of communicating exogenous information by means of Markov decision process trajectories.

Data-Dependent Randomized Smoothing

no code implementations29 Sep 2021 Motasem Alfarra, Adel Bibi, Philip 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.

Shape-Tailored Deep Neural Networks With PDEs

no code implementations NeurIPS Workshop DLDE 2021 Naeemullah Khan, Angira Sharma, Philip Torr, Ganesh Sundaramoorthi

ST-DNN are deep networks formulated through the use of partial differential equations (PDE) to be defined on arbitrarily shaped regions.

Vision Transformer with Progressive Sampling

1 code implementation ICCV 2021 Xiaoyu Yue, Shuyang Sun, Zhanghui Kuang, Meng Wei, Philip Torr, Wayne Zhang, Dahua Lin

As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens.

Image Classification

Multilevel Knowledge Transfer for Cross-Domain Object Detection

no code implementations2 Aug 2021 Botos Csaba, Xiaojuan Qi, Arslan Chaudhry, Puneet Dokania, Philip Torr

The key ingredients to our approach are -- (a) mapping the source to the target domain on pixel-level; (b) training a teacher network on the mapped source and the unannotated target domain using adversarial feature alignment; and (c) finally training a student network using the pseudo-labels obtained from the teacher.

Object Detection Transfer Learning

Open-World Entity Segmentation

2 code implementations29 Jul 2021 Lu Qi, Jason Kuen, Yi Wang, Jiuxiang Gu, Hengshuang Zhao, Zhe Lin, Philip Torr, Jiaya Jia

By removing the need of class label prediction, the models trained for such task can focus more on improving segmentation quality.

Image Manipulation Panoptic Segmentation

Implicit Communication as Minimum Entropy Coupling

no code implementations17 Jul 2021 Samuel Sokota, Christian Schroeder de Witt, Maximilian Igl, Luisa Zintgraf, Philip Torr, Shimon Whiteson, Jakob Foerster

In many common-payoff games, achieving good performance requires players to develop protocols for communicating their private information implicitly -- i. e., using actions that have non-communicative effects on the environment.

Multi-agent Reinforcement Learning

Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation

1 code implementation16 Jul 2021 Angira Sharma, Naeemullah Khan, Muhammad Mubashar, Ganesh Sundaramoorthi, Philip Torr

For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.

Object Detection Salient Object Detection

Visual Parser: Representing Part-whole Hierarchies with Transformers

2 code implementations13 Jul 2021 Shuyang Sun, Xiaoyu Yue, Song Bai, Philip Torr

To model the representations of the two levels, we first encode the information from the whole into part vectors through an attention mechanism, then decode the global information within the part vectors back into the whole representation.

Image Classification Instance Segmentation +2

Large-scale Unsupervised Semantic Segmentation

no code implementations6 Jun 2021 ShangHua Gao, Zhong-Yu Li, Ming-Hsuan Yang, Ming-Ming Cheng, Junwei Han, Philip Torr

Powered by the ImageNet dataset, unsupervised learning on large-scale data has made significant advances for classification tasks.

Representation Learning Unsupervised Semantic Segmentation

General Adversarial Defense via Pixel Level and Feature Level Distribution Alignment

no code implementations1 Jan 2021 Xiaogang Xu, Hengshuang Zhao, Philip Torr, Jiaya Jia

Specifically, compared with previous methods, we propose a more efficient pixel-level training constraint to weaken the hardness of aligning adversarial samples to clean samples, which can thus obviously enhance the robustness on adversarial samples.

Adversarial Defense Image Classification +2

How Benign is Benign Overfitting ?

no code implementations ICLR 2021 Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip Torr

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

Adversarial Robustness Representation Learning

Point Transformer

7 code implementations ICCV 2021 Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip Torr, Vladlen Koltun

For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.

3D Part Segmentation 3D Point Cloud Classification +5

Learning to Sample the Most Useful Training Patches from Images

no code implementations24 Nov 2020 Shuyang Sun, Liang Chen, Gregory Slabaugh, Philip Torr

Some image restoration tasks like demosaicing require difficult training samples to learn effective models.


Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation

1 code implementation28 Oct 2020 Angira Sharma, Naeemullah Khan, Ganesh Sundaramoorthi, Philip Torr

For low-fidelity training data (incorrect class label) class-agnostic segmentation loss outperforms the state-of-the-art methods on salient object detection datasets by staggering margins of around 50%.

Object Detection Salient Object Detection

Simulation-Based Inference for Global Health Decisions

2 code implementations14 May 2020 Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, Atılım Güneş Baydin

The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.

Bayesian Inference Epidemiology

Global Texture Enhancement for Fake Face Detection in the Wild

no code implementations CVPR 2020 Zhengzhe Liu, Xiaojuan Qi, Philip Torr

In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets.

Face Detection Fake Image Detection

Domain-invariant Stereo Matching Networks

1 code implementation ECCV 2020 Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin Wah, Philip Torr

State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast, and texture.

Stereo Matching

Efficient Bayesian Inference for Nested Simulators

no code implementations pproximateinference AABI Symposium 2019 Bradley Gram-Hansen, Christian Schroeder de Witt, Robert Zinkov, Saeid Naderiparizi, Adam Scibior, Andreas Munk, Frank Wood, Mehrdad Ghadiri, Philip Torr, Yee Whye Teh, Atilim Gunes Baydin, Tom Rainforth

We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.

Bayesian Inference

ShardNet: One Filter Set to Rule Them All

no code implementations25 Sep 2019 Saumya Jetley, Tommaso Cavallari, Philip Torr, Stuart Golodetz

Deep CNNs have achieved state-of-the-art performance for numerous machine learning and computer vision tasks in recent years, but as they have become increasingly deep, the number of parameters they use has also increased, making them hard to deploy in memory-constrained environments and difficult to interpret.

Learning Theory

The Intriguing Effects of Focal Loss on the Calibration of Deep Neural Networks

no code implementations25 Sep 2019 Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania

When combined with temperature scaling, focal loss, whilst preserving accuracy and yielding state-of-the-art calibrated models, also preserves the confidence of the model's correct predictions, which is extremely desirable for downstream tasks.

Res2Net: A New Multi-scale Backbone Architecture

18 code implementations2 Apr 2019 Shang-Hua Gao, Ming-Ming Cheng, Kai Zhao, Xin-Yu Zhang, Ming-Hsuan Yang, Philip Torr

We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.

Image Classification Instance Segmentation +2

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

3 code implementations NeurIPS 2019 Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer, Frank Wood

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.

Probabilistic Programming

A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials

no code implementations24 Jan 2017 Måns Larsson, Anurag Arnab, Fredrik Kahl, Shuai Zheng, Philip Torr

It is empirically demonstrated that such learned potentials can improve segmentation accuracy and that certain label class interactions are indeed better modelled by a non-Gaussian potential.

Semantic Segmentation Structured Prediction

Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation

no code implementations7 Dec 2016 Qinbin Hou, Puneet Kumar Dokania, Daniela Massiceti, Yunchao Wei, Ming-Ming Cheng, Philip Torr

We focus on the following three aspects of EM: (i) initialization; (ii) latent posterior estimation (E-step) and (iii) the parameter update (M-step).

Weakly-Supervised Semantic Segmentation

Online Real-time Multiple Spatiotemporal Action Localisation and Prediction

4 code implementations ICCV 2017 Gurkirt Singh, Suman Saha, Michael Sapienza, Philip Torr, Fabio Cuzzolin

To the best of our knowledge, ours is the first real-time (up to 40fps) system able to perform online S/T action localisation and early action prediction on the untrimmed videos of UCF101-24.


Joint Object-Material Category Segmentation from Audio-Visual Cues

no code implementations10 Jan 2016 Anurag Arnab, Michael Sapienza, Stuart Golodetz, Julien Valentin, Ondrej Miksik, Shahram Izadi, Philip Torr

It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials.

Staple: Complementary Learners for Real-Time Tracking

2 code implementations CVPR 2016 Luca Bertinetto, Jack Valmadre, Stuart Golodetz, Ondrej Miksik, Philip Torr

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes.

Visual Object Tracking

Prototypical Priors: From Improving Classification to Zero-Shot Learning

no code implementations3 Dec 2015 Saumya Jetley, Bernardino Romera-Paredes, Sadeep Jayasumana, Philip Torr

Recent works on zero-shot learning make use of side information such as visual attributes or natural language semantics to define the relations between output visual classes and then use these relationships to draw inference on new unseen classes at test time.

Classification General Classification +1

Higher Order Conditional Random Fields in Deep Neural Networks

1 code implementation25 Nov 2015 Anurag Arnab, Sadeep Jayasumana, Shuai Zheng, Philip Torr

Recent deep learning approaches have incorporated CRFs into Convolutional Neural Networks (CNNs), with some even training the CRF end-to-end with the rest of the network.

Semantic Segmentation Superpixels

BING: Binarized Normed Gradients for Objectness Estimation at 300fps

no code implementations CVPR 2014 Ming-Ming Cheng, Ziming Zhang, Wen-Yan Lin, Philip Torr

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm.

Object Detection

Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference

no code implementations20 Apr 2014 Peng Wang, Chunhua Shen, Anton Van Den Hengel, Philip Torr

We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF inference problems.

Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation

no code implementations NeurIPS 2013 Vibhav Vineet, Carsten Rother, Philip Torr

Many methods have been proposed to recover the intrinsic scene properties such as shape, reflectance and illumination from a single image.

ImageSpirit: Verbal Guided Image Parsing

no code implementations16 Oct 2013 Ming-Ming Cheng, Shuai Zheng, Wen-Yan Lin, Jonathan Warrell, Vibhav Vineet, Paul Sturgess, Nigel Crook, Niloy Mitra, Philip Torr

This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images.

Learning Anchor Planes for Classification

no code implementations NeurIPS 2011 Ziming Zhang, Lubor Ladicky, Philip Torr, Amir Saffari

It provides a set of anchor points which form a local coordinate system, such that each data point on the manifold can be approximated by a linear combination of its anchor points, and the linear weights become the local coordinate coding.

Classification General Classification

Improved Moves for Truncated Convex Models

no code implementations NeurIPS 2008 Philip Torr, M. P. Kumar

Compared to previous approaches based on the LP relaxation, e. g. interior-point algorithms or tree-reweighted message passing (TRW), our method is faster as it uses only the efficient st-mincut algorithm in its design.

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