Torr, Philip H. S.

Fully-Convolutional Siamese Networks for Object Tracking

Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016) · Lecture Notes in Computer Science

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 4,546

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 4,008

Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., et al. (2020) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 4,004

Conditional Random Fields as Recurrent Neural Networks

Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., & Torr, P. H. S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsMedical Image Segmentation Techniques

Cited by 2,386

Struck: Structured output tracking with kernels

Hare, S., Saffari, A., & Torr, P. H. S. (2011) · 2011 International Conference on Computer Vision

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 1,831

Fast Online Object Tracking and Segmentation: A Unifying Approach

Wang, Q., Zhang, L., Bertinetto, L., Hu, W., & Torr, P. H. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 1,554

Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence

Chaudhry, A., Dokania, P. K., Ajanthan, T., & Torr, P. H. S. (2018) · Lecture Notes in Computer Science

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 1,091

SNIP: Single-shot Network Pruning based on Connection Sensitivity

Lee, N., Ajanthan, T., & Torr, P. H. S. (2018) · arXiv

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 685

Global Texture Enhancement for Fake Face Detection in the Wild

Liu, Z., Qi, X., & Torr, P. H. S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 401

Learn To Pay Attention

Jetley, S., Lord, N. A., Lee, N., & Torr, P. H. S. (2018) · arXiv

Visual Attention and Saliency DetectionAdvanced Neural Network Applications

Cited by 383

Managing extreme AI risks amid rapid progress

Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., Harari, Y. N., Zhang, Y.-Q., Xue, L., Shalev-Shwartz, S., et al. (2024) · Science

Ethics and Social Impacts of AIDigitalization, Law, and Regulation

Cited by 349

End-to-End Representation Learning for Correlation Filter Based Tracking

Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., & Torr, P. H. S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 274

Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction

Vineet, V., Miksik, O., Lidegaard, M., Niebner, M., Golodetz, S., Prisacariu, V. A., Kahler, O., Murray, D. W., Izadi, S., Peerez, P., et al. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA)

Advanced Vision and ImagingRobotics and Sensor-Based Localization

Cited by 200

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

Shi, Y., Siddharth, N., Paige, B., & Torr, P. H. S. (2019) · arXiv

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 186

Weakly- and Semi-supervised Panoptic Segmentation

Li, Q., Arnab, A., & Torr, P. H. S. (2018) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 171

Exploiting uncertainty in regression forests for accurate camera relocalization

Valentin, J., Niebner, M., Shotton, J., Fitzgibbon, A., Izadi, S., & Torr, P. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Robotics and Sensor-Based LocalizationAdvanced Vision and Imaging

Cited by 156

Deep Deterministic Uncertainty: A New Simple Baseline

Mukhoti, J., Kirsch, A., van Amersfoort, J., Torr, P. H. S., & Gal, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Adversarial Robustness in Machine LearningAdvanced Neural Network Applications

Cited by 116

Siam R-CNN: Visual Tracking by Re-Detection

Voigtlaender, P., Luiten, J., Torr, P. H. S., & Leibe, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 115

Measuring what Matters: Construct Validity in Large Language Model Benchmarks

Bean, A. M., Kearns, R. O., Romanou, A., Hafner, F. S., Mayne, H., Batzner, J., Foroutan Eghlidi, N., Schmitz, C., Korgul, K., Batra, H., et al. (2025) · Advances in Neural Information Processing Systems 38

Topic ModelingNatural Language Processing Techniques

Cited by 110

Secret Collusion among AI Agents: Multi-Agent Deception via Steganography

Motwani, S., Baranchuk, M., Strohmeier, M., Bolina, V., Torr, P., Hammond, L., & Witt, C. D. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningAdvanced Steganography and Watermarking Techniques

Cited by 106

Large-Scale Unsupervised Semantic Segmentation

Gao, S., Li, Z.-Y., Yang, M.-H., Cheng, M.-M., Han, J., & Torr, P. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 88

Continual Learning in Low-rank Orthogonal Subspaces

Chaudhry, A., Khan, N., Dokania, P. K., & Torr, P. H. S. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 54

Can Large Language Model Agents Simulate Human Trust Behavior?

Xie, C., Chen, C., Jia, F., Ye, Z., Lai, S., Shu, K., Gu, J., Bibi, A., Hu, Z., Jurgens, D., et al. (2024) · Advances in Neural Information Processing Systems 37

Social Robot Interaction and HRIMulti-Agent Systems and Negotiation

Cited by 31

Vision Transformers: From Semantic Segmentation to Dense Prediction

Zhang, L., Lu, J., Zheng, S., Zhao, X., Zhu, X., Fu, Y., Xiang, T., Feng, J., & Torr, P. H. S. (2024) · International Journal of Computer Vision

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 23