Philip Torr
Professor of Engineering Science
Leads the Torr Vision Group on computer vision and machine learning, including robust and trustworthy deep learning.
Philip Torr's lab at Oxford works on trustworthy & safe ML and interpretability, evaluation & safety of LMs. 33 papers since 2023, 19 at top venues, mostly ICLR and ECCV. 56 alumni. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 58%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 56
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 0 papers2025 · T1 0
- 20 papers2024 · T1 14
- 13 papers2023 · T1 5
T1 venuesOtherVenues: ICLR, ECCV, NeurIPS, AAAI, BMVC
Research focuspaper titles that use each topic's words
- vision3 papers
- Interpretability, evaluation & safety of LMs2 papers
- Trustworthy & safe ML1 paper
- engineering science0 papers
- robust0 papers
Paper list
From the lab homepage.
202420 papers
- TMLR (T1)A Survey on Transferability of Adversarial Examples across Deep Neural Networks (opens in a new tab)
- Public Policy, University of SouthamptonAI governance: A response to UN interim report on governing AI for humanity (opens in a new tab)
- ICLR (T1)An Image Is Worth 1000 Lies: Transferability of Adversarial Images across Prompts on Vision-Language Models (opens in a new tab)
- BMVCAs Firm As Their Foundations: Creating Transferable Adversarial Examples Across Downstream Tasks with CLIP (opens in a new tab)
- TPAMI (T1)Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation (opens in a new tab)
- WACVCan Multimodal Large Language Models Truly Perform Multimodal In-Context Learning? (opens in a new tab)
- ECCV (T1)Improving Adversarial Transferability via Model Alignment (opens in a new tab)
- ICLR (T1)Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images (opens in a new tab)
- ICLR (T1)Influencer Backdoor Attack on Semantic Segmentation (opens in a new tab)
- NeurIPS (T1)Interpreting Learned Feedback Patterns in Large Language Models (opens in a new tab)
- ECCV (T1)Latent Guard: a Safety Framework for Text-to-image Generation (opens in a new tab)
- ICML (T1)Prompting a Pretrained Transformer Can Be a Universal Approximator (opens in a new tab)
202313 papers
- NeurIPS Dataset and Benchmark TrackBenchmarking Robustness of Adaptation Methods on Pre-trained VLMs (opens in a new tab)
- ICML (T1)Certifying Ensembles: A General Certification Theory with S-Lipschitzness (opens in a new tab)
- CVPR (T1)Computationally Budgeted Continual Learning: What Does Matter? (opens in a new tab)
- AAAI (T1)Deeply Explain CNN via Hierarchical Decomposition (opens in a new tab)
- BMVCExploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks (opens in a new tab)
- Annual Stanford Existential Risks ConferenceFairness in AI and Its Long-Term Implications on Society (opens in a new tab)
- NeurIPS (T1)Language Model Tokenizers Introduce Unfairness Between Languages (opens in a new tab)
- IEEE Conference of Computer Vision and Pattern RecognitionLinear Complexity Self-Attention with 3rd Order Polynomials (opens in a new tab)
- WACVRANCER: Non-Axis Aligned Anisotropic Certification With Randomized Smoothing (opens in a new tab)
- Oral in CVPRReal-Time Evaluation in Online Continual Learning: A New Hope (opens in a new tab)
- WANT, NeurIPSRemaining-Useful-Life Prediction and Uncertainty Quantification using LSTM Ensembles for Aircraft Engines (opens in a new tab)
- AAAI (T1)Sample-dependent Adaptive Temperature Scaling for Improved Calibration (opens in a new tab)
Where alumni went56 listed
Apply to Oxford2027-28
DPhil in Computer Science
- Round
- 2027-28 entry (January round)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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- The lab homepage
- Members
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- Lab homepage
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