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ORCIDPhilip H. S. Torr
Philip Hilaire Sean Torr
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- affiliation: University of Oxford
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2020 – today
- 2021
[j64]Song Bai, Feihu Zhang, Philip H. S. Torr:
Hypergraph convolution and hypergraph attention. Pattern Recognit. 110: 107637 (2021)- 2020
- [j63]Rodrigo de Bem
, Arnab Ghosh, Thalaiyasingam Ajanthan, Ondrej Miksik, Adnane Boukhayma, N. Siddharth, Philip H. S. Torr:
DGPose: Deep Generative Models for Human Body Analysis. Int. J. Comput. Vis. 128(5): 1537-1563 (2020) - [j62]Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar:
Branch and Bound for Piecewise Linear Neural Network Verification. J. Mach. Learn. Res. 21: 42:1-42:39 (2020) - [j61]Juan-Manuel Pérez-Rúa, Ondrej Miksik, Tomás Crivelli, Patrick Bouthemy, Philip H. S. Torr, Patrick Pérez:
ROAM: A Rich Object Appearance Model with Application to Rotoscoping. IEEE Trans. Pattern Anal. Mach. Intell. 42(8): 1996-2010 (2020) - [j60]Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien P. C. Valentin, Victor Adrian Prisacariu, Luigi di Stefano, Philip H. S. Torr:
Real-Time RGB-D Camera Pose Estimation in Novel Scenes Using a Relocalisation Cascade. IEEE Trans. Pattern Anal. Mach. Intell. 42(10): 2465-2477 (2020) - [j59]Anurag Arnab, Ondrej Miksik, Philip H. S. Torr:
On the Robustness of Semantic Segmentation Models to Adversarial Attacks. IEEE Trans. Pattern Anal. Mach. Intell. 42(12): 3040-3053 (2020) - [c227]Nan Xue, Tianfu Wu, Song Bai, Fudong Wang, Gui-Song Xia, Liangpei Zhang, Philip H. S. Torr:
Holistically-Attracted Wireframe Parsing. CVPR 2020: 2785-2794 - [c226]Li Zhang, Dan Xu, Anurag Arnab, Philip H. S. Torr:
Dynamic Graph Message Passing Networks. CVPR 2020: 3723-3732 - [c225]Victoria Fernández Abrevaya, Adnane Boukhayma, Philip H. S. Torr, Edmond Boyer:
Cross-Modal Deep Face Normals With Deactivable Skip Connections. CVPR 2020: 4978-4988 - [c224]Paul Voigtlaender, Jonathon Luiten, Philip H. S. Torr, Bastian Leibe:
Siam R-CNN: Visual Tracking by Re-Detection. CVPR 2020: 6577-6587 - [c223]Hao Tang, Dan Xu, Yan Yan, Philip H. S. Torr, Nicu Sebe:
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation. CVPR 2020: 7867-7876 - [c222]Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr:
ManiGAN: Text-Guided Image Manipulation. CVPR 2020: 7877-7886 - [c221]Zhengzhe Liu, Xiaojuan Qi, Philip H. S. Torr:
Global Texture Enhancement for Fake Face Detection in the Wild. CVPR 2020: 8057-8066 - [c220]Qizhu Li, Xiaojuan Qi, Philip H. S. Torr:
Unifying Training and Inference for Panoptic Segmentation. CVPR 2020: 13317-13325 - [c219]Carlo Biffi, Steven McDonagh, Philip H. S. Torr, Ales Leonardis, Sarah Parisot:
Many-Shot from Low-Shot: Learning to Annotate Using Mixed Supervision for Object Detection. ECCV (8) 2020: 35-50 - [c218]Harkirat Singh Behl, Atilim Günes Baydin, Ran Gal, Philip H. S. Torr, Vibhav Vineet:
AutoSimulate: (Quickly) Learning Synthetic Data Generation. ECCV (22) 2020: 255-271 - [c217]Viveka Kulharia, Siddhartha Chandra, Amit Agrawal, Philip H. S. Torr, Ambrish Tyagi:
Box2Seg: Attention Weighted Loss and Discriminative Feature Learning for Weakly Supervised Segmentation. ECCV (27) 2020: 290-308 - [c216]Feihu Zhang, Xiaojuan Qi, Ruigang Yang, Victor Prisacariu, Benjamin W. Wah, Philip H. S. Torr:
Domain-Invariant Stereo Matching Networks. ECCV (2) 2020: 420-439 - [c215]Ameya Prabhu, Philip H. S. Torr, Puneet K. Dokania:
GDumb: A Simple Approach that Questions Our Progress in Continual Learning. ECCV (2) 2020: 524-540 - [c214]Hongguang Zhang, Li Zhang, Xiaojuan Qi, Hongdong Li, Philip H. S. Torr, Piotr Koniusz:
Few-Shot Action Recognition with Permutation-Invariant Attention. ECCV (5) 2020: 525-542 - [c213]Hao Tang, Song Bai, Li Zhang, Philip H. S. Torr, Nicu Sebe:
XingGAN for Person Image Generation. ECCV (25) 2020: 717-734 - [c212]Oscar Rahnama, Tommaso Cavallari, Philip H. S. Torr, Stuart Golodetz:
Scalable FPGA Median Filtering using Multiple Efficient Passes. FPGA 2020: 313 - [c211]Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr:
A Signal Propagation Perspective for Pruning Neural Networks at Initialization. ICLR 2020 - [c210]Amartya Sanyal, Philip H. S. Torr, Puneet K. Dokania:
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs. ICLR 2020 - [c209]Feihu Zhang, Chenye Guan, Jin Fang, Song Bai, Ruigang Yang, Philip H. S. Torr, Victor Prisacariu:
Instance Segmentation of LiDAR Point Clouds. ICRA 2020: 9448-9455 - [c208]Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. UAI 2020: 159 - [c207]Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr:
Devon: Deformable Volume Network for Learning Optical Flow. WACV 2020: 2694-2702 - [i143]Hongguang Zhang, Philip H. S. Torr, Piotr Koniusz:
Few-shot Learning with Multi-scale Self-supervision. CoRR abs/2001.01600 (2020) - [i142]Hongguang Zhang, Li Zhang, Xiaojuan Qi, Hongdong Li, Philip H. S. Torr, Piotr Koniusz:
Few-shot Action Recognition via Improved Attention with Self-supervision. CoRR abs/2001.03905 (2020) - [i141]Hongguang Zhang, Philip H. S. Torr, Hongdong Li, Songlei Jian, Piotr Koniusz:
Rethinking Class Relations: Absolute-relative Few-shot Learning. CoRR abs/2001.03919 (2020) - [i140]Qizhu Li, Xiaojuan Qi, Philip H. S. Torr:
Unifying Training and Inference for Panoptic Segmentation. CoRR abs/2001.04982 (2020) - [i139]Zhengzhe Liu, Xiaojuan Qi, Jiaya Jia, Philip H. S. Torr:
Global Texture Enhancement for Fake Face Detection in the Wild. CoRR abs/2002.00133 (2020) - [i138]Hao Tang, Dan Xu, Yan Yan, Jason J. Corso, Philip H. S. Torr, Nicu Sebe:
Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation. CoRR abs/2002.01048 (2020) - [i137]Bowen Li, Xiaojuan Qi, Philip H. S. Torr, Thomas Lukasiewicz:
Image-to-Image Translation with Text Guidance. CoRR abs/2002.05235 (2020) - [i136]Arslan Chaudhry, Albert Gordo, Puneet K. Dokania, Philip H. S. Torr, David Lopez-Paz:
Using Hindsight to Anchor Past Knowledge in Continual Learning. CoRR abs/2002.08165 (2020) - [i135]Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip H. S. Torr, Puneet K. Dokania:
Calibrating Deep Neural Networks using Focal Loss. CoRR abs/2002.09437 (2020) - [i134]Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. CoRR abs/2002.10410 (2020) - [i133]Nan Xue, Tianfu Wu, Song Bai, Fudong Wang, Gui-Song Xia, Liangpei Zhang, Philip H. S. Torr:
Holistically-Attracted Wireframe Parsing. CoRR abs/2003.01663 (2020) - [i132]Christian Schröder de Witt, Bei Peng, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Böhmer, Shimon Whiteson:
Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control. CoRR abs/2003.06709 (2020) - [i131]Victoria Fernández Abrevaya, Adnane Boukhayma, Philip H. S. Torr, Edmond Boyer:
Cross-modal Deep Face Normals with Deactivable Skip Connections. CoRR abs/2003.09691 (2020) - [i130]Namhoon Lee, Philip H. S. Torr, Martin Jaggi:
Data Parallelism in Training Sparse Neural Networks. CoRR abs/2003.11316 (2020) - [i129]Hao Tang, Xiaojuan Qi, Dan Xu, Philip H. S. Torr, Nicu Sebe:
Edge Guided GANs with Semantic Preserving for Semantic Image Synthesis. CoRR abs/2003.13898 (2020) - [i128]Daniela Massiceti, Viveka Kulharia, Puneet K. Dokania, N. Siddharth, Philip H. S. Torr:
A Revised Generative Evaluation of Visual Dialogue. CoRR abs/2004.09272 (2020) - [i127]Christian Schröder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Robert Zinkov, Puneet K. Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip H. S. Torr, Atilim Günes Baydin:
Simulation-Based Inference for Global Health Decisions. CoRR abs/2005.07062 (2020) - [i126]Pau de Jorge, Amartya Sanyal, Harkirat S. Behl, Philip H. S. Torr, Grégory Rogez, Puneet K. Dokania:
Progressive Skeletonization: Trimming more fat from a network at initialization. CoRR abs/2006.09081 (2020) - [i125]Tom Joy, Sebastian M. Schmon, Philip H. S. Torr, N. Siddharth, Tom Rainforth:
Rethinking Semi-Supervised Learning in VAEs. CoRR abs/2006.10102 (2020) - [i124]Arnab Ghosh, Harkirat Singh Behl, Emilien Dupont, Philip H. S. Torr, Vinay Namboodiri:
STEER : Simple Temporal Regularization For Neural ODEs. CoRR abs/2006.10711 (2020) - [i123]Yuge Shi, Brooks Paige, Philip H. S. Torr, N. Siddharth:
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models. CoRR abs/2007.01179 (2020) - [i122]Amartya Sanyal, Puneet K. Dokania, Varun Kanade, Philip H. S. Torr:
How benign is benign overfitting? CoRR abs/2007.04028 (2020) - [i121]Minqi Jiang, Jelena Luketina, Nantas Nardelli, Pasquale Minervini, Philip H. S. Torr, Shimon Whiteson, Tim Rocktäschel:
WordCraft: An Environment for Benchmarking Commonsense Agents. CoRR abs/2007.09185 (2020) - [i120]Hao Tang, Song Bai, Li Zhang, Philip H. S. Torr, Nicu Sebe:
XingGAN for Person Image Generation. CoRR abs/2007.09278 (2020) - [i119]Hao Tang, Song Bai, Philip H. S. Torr, Nicu Sebe:
Bipartite Graph Reasoning GANs for Person Image Generation. CoRR abs/2008.04381 (2020) - [i118]Harkirat Singh Behl, Atilim Günes Baydin, Ran Gal, Philip H. S. Torr, Vibhav Vineet:
AutoSimulate: (Quickly) Learning Synthetic Data Generation. CoRR abs/2008.08424 (2020) - [i117]Carlo Biffi, Steven McDonagh, Philip H. S. Torr, Ales Leonardis, Sarah Parisot:
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection. CoRR abs/2008.09694 (2020) - [i116]Jonathon Luiten, Aljosa Osep, Patrick Dendorfer, Philip H. S. Torr, Andreas Geiger, Laura Leal-Taixé, Bastian Leibe:
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking. CoRR abs/2009.07736 (2020) - [i115]Thomas Tanay, Aivar Sootla, Matteo Maggioni, Puneet K. Dokania, Philip H. S. Torr, Ales Leonardis, Gregory G. Slabaugh:
Diagnosing and Preventing Instabilities in Recurrent Video Processing. CoRR abs/2010.05099 (2020) - [i114]Arslan Chaudhry, Naeemullah Khan, Puneet K. Dokania, Philip H. S. Torr:
Continual Learning in Low-rank Orthogonal Subspaces. CoRR abs/2010.11635 (2020) - [i113]Bowen Li, Xiaojuan Qi, Philip H. S. Torr, Thomas Lukasiewicz:
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation. CoRR abs/2010.12136 (2020) - [i112]Angira Sharma, Naeemullah Khan, Ganesh Sundaramoorthi, Philip H. S. Torr:
Class-Agnostic Segmentation Loss and Its Application to Salient Object Detection and Segmentation. CoRR abs/2010.14793 (2020)
2010 – 2019
- 2019
- [j58]Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr:
BING: Binarized normed gradients for objectness estimation at 300fps. Comput. Vis. Media 5(1): 3-20 (2019) - [j57]Qibin Hou, Ming-Ming Cheng, Xiaowei Hu, Ali Borji, Zhuowen Tu, Philip H. S. Torr:
Deeply Supervised Salient Object Detection with Short Connections. IEEE Trans. Pattern Anal. Mach. Intell. 41(4): 815-828 (2019) - [j56]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. SIAM J. Imaging Sci. 12(1): 287-318 (2019) - [j55]Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Alessio Tonioni, Thomas Joy, Luigi di Stefano, Simon Walker, Philip H. S. Torr:
Real-Time Highly Accurate Dense Depth on a Power Budget Using an FPGA-CPU Hybrid SoC. IEEE Trans. Circuits Syst. II Express Briefs 66-II(5): 773-777 (2019) - [c206]Tommaso Cavallari, Luca Bertinetto, Jishnu Mukhoti, Philip H. S. Torr, Stuart Golodetz:
Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation. 3DV 2019: 564-573 - [c205]Mikayel Samvelyan, Tabish Rashid, Christian Schröder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob N. Foerster, Shimon Whiteson:
The StarCraft Multi-Agent Challenge. AAMAS 2019: 2186-2188 - [c204]Li Zhang, Xiangtai Li, Anurag Arnab, Kuiyuan Yang, Yunhai Tong, Philip H. S. Torr:
Dual Graph Convolutional Network for Semantic Segmentation. BMVC 2019: 254 - [c203]Feihu Zhang, Victor Adrian Prisacariu, Ruigang Yang, Philip H. S. Torr:
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching. CVPR 2019: 185-194 - [c202]Song Bai, Peng Tang, Philip H. S. Torr, Longin Jan Latecki:
Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification. CVPR 2019: 740-749 - [c201]Qiang Wang, Li Zhang, Luca Bertinetto, Weiming Hu, Philip H. S. Torr:
Fast Online Object Tracking and Segmentation: A Unifying Approach. CVPR 2019: 1328-1338 - [c200]Eunwoo Kim, Chanho Ahn, Philip H. S. Torr, Songhwai Oh:
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks. CVPR 2019: 2710-2719 - [c199]Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi di Stefano, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Learning to Adapt for Stereo. CVPR 2019: 9661-9670 - [c198]Adnane Boukhayma, Rodrigo de Bem, Philip H. S. Torr:
3D Hand Shape and Pose From Images in the Wild. CVPR 2019: 10843-10852 - [c197]Zhao Yang, Qiang Wang, Luca Bertinetto, Song Bai, Weiming Hu, Philip H. S. Torr:
Anchor Diffusion for Unsupervised Video Object Segmentation. ICCV 2019: 931-940 - [c196]Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H. S. Torr, Eli Shechtman:
Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation. ICCV 2019: 1171-1180 - [c195]Thalaiyasingam Ajanthan, Puneet K. Dokania, Richard Hartley, Philip H. S. Torr:
Proximal Mean-Field for Neural Network Quantization. ICCV 2019: 4870-4879 - [c194]Jonathon Luiten, Philip H. S. Torr, Bastian Leibe:
Video Instance Segmentation 2019: A Winning Approach for Combined Detection, Segmentation, Classification and Tracking. ICCV Workshops 2019: 709-712 - [c193]Qiang Wang, Yi He, Xiaoyun Yang, Zhao Yang, Philip H. S. Torr:
An Empirical Study of Detection-Based Video Instance Segmentation. ICCV Workshops 2019: 713-716 - [c192]Matej Kristan, Amanda Berg, Linyu Zheng, Litu Rout, Luc Van Gool, Luca Bertinetto, Martin Danelljan, Matteo Dunnhofer, Meng Ni, Min Young Kim, Ming Tang, Ming-Hsuan Yang, Abdelrahman Eldesokey, Naveen Paluru, Niki Martinel, Pengfei Xu, Pengfei Zhang, Pengkun Zheng, Pengyu Zhang, Philip H. S. Torr, Qi Zhang, Qiang Wang, Qing Guo, Radu Timofte, Jani Käpylä, Rama Krishna Sai Subrahmanyam Gorthi, Richard M. Everson, Ruize Han, Ruohan Zhang, Shan You, Shao-Chuan Zhao, Shengwei Zhao, Shihu Li, Shikun Li, Shiming Ge, Gustavo Fernández, Shuai Bai, Shuosen Guan, Tengfei Xing, Tianyang Xu, Tianyu Yang, Ting Zhang, Tomás Vojír, Wei Feng, Weiming Hu, Weizhao Wang, Abel Gonzalez-Garcia, Wenjie Tang, Wenjun Zeng, Wenyu Liu, Xi Chen, Xi Qiu, Xiang Bai, Xiao-Jun Wu, Xiaoyun Yang, Xier Chen, Xin Li, Alireza Memarmoghadam, Xing Sun, Xingyu Chen, Xinmei Tian, Xu Tang, Xuefeng Zhu, Yan Huang, Yanan Chen, Yanchao Lian, Yang Gu, Yang Liu, Andong Lu, Yanjie Chen, Yi Zhang, Yinda Xu, Yingming Wang, Yingping Li, Yu Zhou, Yuan Dong, Yufei Xu, Yunhua Zhang, Yunkun Li, Anfeng He, Zeyu Wang Zhao Luo, Zhaoliang Zhang, Zhen-Hua Feng, Zhenyu He, Zhichao Song, Zhihao Chen, Zhipeng Zhang, Zhirong Wu, Zhiwei Xiong, Zhongjian Huang, Anton Varfolomieiev, Zhu Teng, Zihan Ni, Antoni B. Chan, Jiri Matas, Ardhendu Shekhar Tripathi, Arnold W. M. Smeulders, Bala Suraj Pedasingu, Bao Xin Chen, Baopeng Zhang, Baoyuan Wu, Bi Li, Bin He, Bin Yan, Bing Bai, Ales Leonardis, Bing Li, Bo Li, Byeong Hak Kim, Chao Ma, Chen Fang, Chen Qian, Cheng Chen, Chenglong Li, Chengquan Zhang, Chi-Yi Tsai, Michael Felsberg, Chong Luo, Christian Micheloni, Chunhui Zhang, Dacheng Tao, Deepak Gupta, Dejia Song, Dong Wang, Efstratios Gavves, Eunu Yi, Fahad Shahbaz Khan, Roman P. Pflugfelder, Fangyi Zhang, Fei Wang, Fei Zhao, George De Ath, Goutam Bhat, Guangqi Chen, Guangting Wang, Guoxuan Li, Hakan Cevikalp, Hao Du, Joni-Kristian Kämäräinen, Haojie Zhao, Hasan Saribas, Ho Min Jung, Hongliang Bai, Hongyuan Yu, Houwen Peng, Huchuan Lu, Hui Li, Jiakun Li, Luka Cehovin Zajc, Jianhua Li, Jianlong Fu, Jie Chen, Jie Gao, Jie Zhao, Jin Tang, Jing Li, Jingjing Wu, Jingtuo Liu, Jinqiao Wang, Ondrej Drbohlav, Jinqing Qi, Jinyue Zhang, John K. Tsotsos, Jong Hyuk Lee, Joost van de Weijer, Josef Kittler, Jun Ha Lee, Junfei Zhuang, Kangkai Zhang, Kangkang Wang, Alan Lukezic, Kenan Dai, Lei Chen, Lei Liu, Leida Guo, Li Zhang, Liang Wang, Liangliang Wang, Lichao Zhang, Lijun Wang, Lijun Zhou:
The Seventh Visual Object Tracking VOT2019 Challenge Results. ICCV Workshops 2019: 2206-2241 - [c191]Luca Bertinetto, João F. Henriques, Philip H. S. Torr, Andrea Vedaldi:
Meta-learning with differentiable closed-form solvers. ICLR (Poster) 2019 - [c190]Namhoon Lee, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Snip: single-Shot Network Pruning based on Connection sensitivity. ICLR (Poster) 2019 - [c189]Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H. S. Torr, Nicolas Usunier:
Value Propagation Networks. ICLR (Poster) 2019 - [c188]Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr:
Controllable Text-to-Image Generation. NeurIPS 2019: 2063-2073 - [c187]Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. NeurIPS 2019: 5460-5473 - [c186]Christian Schröder de Witt, Jakob N. Foerster, Gregory Farquhar, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson:
Multi-Agent Common Knowledge Reinforcement Learning. NeurIPS 2019: 9924-9935 - [c185]Yuge Shi, Siddharth Narayanaswamy, Brooks Paige, Philip H. S. Torr:
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. NeurIPS 2019: 15692-15703 - [c184]Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Etalumis: bringing probabilistic programming to scientific simulators at scale. SC 2019: 29:1-29:24 - [c183]Rodrigo de Bem, Arnab Ghosh, Adnane Boukhayma, Thalaiyasingam Ajanthan, N. Siddharth, Philip H. S. Torr:
A Conditional Deep Generative Model of People in Natural Images. WACV 2019: 1449-1458 - [i111]Song Bai, Feihu Zhang, Philip H. S. Torr:
Hypergraph Convolution and Hypergraph Attention. CoRR abs/1901.08150 (2019) - [i110]Song Bai, Yingwei Li, Yuyin Zhou, Qizhu Li, Philip H. S. Torr:
Adversarial Metric Attack for Person Re-identification. CoRR abs/1901.10650 (2019) - [i109]Adnane Boukhayma, Rodrigo de Bem, Philip H. S. Torr:
3D Hand Shape and Pose from Images in the Wild. CoRR abs/1902.03451 (2019) - [i108]Mikayel Samvelyan, Tabish Rashid, Christian Schröder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob N. Foerster, Shimon Whiteson:
The StarCraft Multi-Agent Challenge. CoRR abs/1902.04043 (2019) - [i107]Botos Csaba, Adnane Boukhayma, Viveka Kulharia, András Horváth, Philip H. S. Torr:
Domain Partitioning Network. CoRR abs/1902.08134 (2019) - [i106]Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet Kumar Dokania, Philip H. S. Torr, Marc'Aurelio Ranzato:
Continual Learning with Tiny Episodic Memories. CoRR abs/1902.10486 (2019) - [i105]Shanghua Gao, Ming-Ming Cheng, Kai Zhao, Xinyu Zhang, Ming-Hsuan Yang, Philip H. S. Torr:
Res2Net: A New Multi-scale Backbone Architecture. CoRR abs/1904.01169 (2019) - [i104]Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi di Stefano, Thalaiyasingam Ajanthan, Philip H. S. Torr:
Learning to Adapt for Stereo. CoRR abs/1904.02957 (2019) - [i103]Eunwoo Kim, Chanho Ahn, Philip H. S. Torr, Songhwai Oh:
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks. CoRR abs/1904.04562 (2019) - [i102]Feihu Zhang, Victor Adrian Prisacariu, Ruigang Yang, Philip H. S. Torr:
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching. CoRR abs/1904.06587 (2019) - [i101]Harkirat Singh Behl, Atilim Günes Baydin, Philip H. S. Torr:
Alpha MAML: Adaptive Model-Agnostic Meta-Learning. CoRR abs/1905.07435 (2019) - [i100]Laurynas Miksys, Saumya Jetley, Michael Sapienza, Stuart Golodetz, Philip H. S. Torr:
Straight to Shapes++: Real-time Instance Segmentation Made More Accurate. CoRR abs/1905.11358 (2019) - [i99]Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth, Philip H. S. Torr, Yee Whye Teh, Atilim Günes Baydin:
Hijacking Malaria Simulators with Probabilistic Programming. CoRR abs/1905.12432 (2019) - [i98]Amartya Sanyal, Philip H. S. Torr, Puneet K. Dokania:
Stable Rank Normalization for Improved Generalization in Neural Networks and GANs. CoRR abs/1906.04659 (2019) - [i97]Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, Philip H. S. Torr:
A Signal Propagation Perspective for Pruning Neural Networks at Initialization. CoRR abs/1906.06307 (2019) - [i96]Tommaso Cavallari, Luca Bertinetto, Jishnu Mukhoti, Philip H. S. Torr, Stuart Golodetz:
Let's Take This Online: Adapting Scene Coordinate Regression Network Predictions for Online RGB-D Camera Relocalisation. CoRR abs/1906.08744 (2019) - [i95]Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood:
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale. CoRR abs/1907.03382 (2019) - [i94]Oscar Rahnama, Tommaso Cavallari, Stuart Golodetz, Alessio Tonioni, Thomas Joy, Luigi di Stefano, Simon Walker, Philip H. S. Torr:
Real-Time Highly Accurate Dense Depth on a Power Budget using an FPGA-CPU Hybrid SoC. CoRR abs/1907.07745 (2019) - [i93]Li Zhang, Dan Xu, Anurag Arnab, Philip H. S. Torr:
Dynamic Graph Message Passing Networks. CoRR abs/1908.06955 (2019) - [i92]Li Zhang, Xiangtai Li, Anurag Arnab, Kuiyuan Yang, Yunhai Tong, Philip H. S. Torr:
Dual Graph Convolutional Network for Semantic Segmentation. CoRR abs/1909.06121 (2019) - [i91]Rudy Bunel, Jingyue Lu, Ilker Turkaslan, Philip H. S. Torr, Pushmeet Kohli, M. Pawan Kumar:
Branch and Bound for Piecewise Linear Neural Network Verification. CoRR abs/1909.06588 (2019) - [i90]Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr:
Controllable Text-to-Image Generation. CoRR abs/1909.07083 (2019) - [i89]Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H. S. Torr, Eli Shechtman:
Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation. CoRR abs/1909.11081 (2019) - [i88]Thalaiyasingam Ajanthan, Kartik Gupta, Philip H. S. Torr, Richard Hartley, Puneet K. Dokania:
Mirror Descent View for Neural Network Quantization. CoRR abs/1910.08237 (2019) - [i87]Saeid Naderiparizi, Adam Scibior, Andreas Munk, Mehrdad Ghadiri, Atilim Günes Baydin, Bradley Gram-Hansen, Christian Schröder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood:
Amortized Rejection Sampling in Universal Probabilistic Programming. CoRR abs/1910.09056 (2019) - [i86]Zhao Yang, Qiang Wang, Luca Bertinetto, Weiming Hu, Song Bai, Philip H. S. Torr:
Anchor Diffusion for Unsupervised Video Object Segmentation. CoRR abs/1910.10895 (2019) - [i85]Yuge Shi, N. Siddharth, Brooks Paige, Philip H. S. Torr:
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models. CoRR abs/1911.03393 (2019) - [i84]Hao Tang, Hong Liu, Dan Xu, Philip H. S. Torr, Nicu Sebe:
AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks. CoRR abs/1911.11897 (2019) - [i83]Paul Voigtlaender, Jonathon Luiten, Philip H. S. Torr, Bastian Leibe:
Siam R-CNN: Visual Tracking by Re-Detection. CoRR abs/1911.12836 (2019) - [i82]Andrew Gambardella, Atilim Günes Baydin, Philip H. S. Torr:
Transflow Learning: Repurposing Flow Models Without Retraining. CoRR abs/1911.13270 (2019) - [i81]