Hospedales, T.
* 2019: Robust Person Re-Identification by Modelling Feature Uncertainty
* 2024: Editorial: Learning With Fewer Labels in Computer Vision
* 2024: Feed-Forward Latent Domain Adaptation
* 2024: Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
Includes: Hospedales, T. Hospedales, T.[Timothy]
Hospedales, T.M.
* 2008: Adaptive Machine Director, An
* 2008: Structure Inference for Bayesian Multisensory Scene Understanding
* 2009: Markov Clustering Topic Model for Mining Behaviour in Video, A
* 2009: Unified Bayesian Framework for Adaptive Visual Tracking, A
* 2010: Learning Rare Behaviours
* 2011: Identifying Rare and Subtle Behaviors: A Weakly Supervised Joint Topic Model
* 2012: Attribute Learning for Understanding Unstructured Social Activity
* 2012: Person Re-identification by Attributes
* 2012: Stream-based joint exploration-exploitation active learning
* 2012: Towards Person Identification and Re-identification with Attributes
* 2012: Unifying Theory of Active Discovery and Learning, A
* 2012: Video Behaviour Mining Using a Dynamic Topic Model
* 2013: Bayesian Joint Topic Modelling for Weakly Supervised Object Localisation
* 2014: Cross-Modal Face Matching: Beyond Viewed Sketches
* 2014: Interestingness Prediction by Robust Learning to Rank
* 2014: Intra-category sketch-based image retrieval by matching deformable part models
* 2014: Investigating Open-World Person Re-identification Using a Drone
* 2014: Learning Multimodal Latent Attributes
* 2014: Open-world Person Re-Identification by Multi-Label Assignment Inference
* 2014: Re-id: Hunting Attributes in the Wild
* 2014: Transductive Multi-label Zero-shot Learning
* 2014: Transductive Multi-view Embedding for Zero-Shot Recognition and Annotation
* 2014: Weakly Supervised Learning of Objects, Attributes and Their Associations
* 2015: Bayesian Joint Modelling for Object Localisation in Weakly Labelled Images
* 2015: Free-hand sketch recognition by multi-kernel feature learning
* 2015: Making better use of edges via perceptual grouping
* 2015: Semantic embedding space for zero-shot action recognition
* 2015: Sketch-a-Net that Beats Humans
* 2015: Transductive Multi-View Zero-Shot Learning
* 2015: Transferring a semantic representation for person re-identification and search
* 2015: When Face Recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face Recognition
* 2015: Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian
* 2016: Deep Multi-task Attribute-Driven Ranking for Fine-Grained Sketch-Based Image Retrieval
* 2016: Fine-Grained Sketch-Based Image Retrieval: The Role of Part-Aware Attributes
* 2016: ForgetMeNot: Memory-Aware Forensic Facial Sketch Matching
* 2016: L_1 Graph Based Sparse Model for Label De-noising
* 2016: Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation
* 2016: Multivariate Regression on the Grassmannian for Predicting Novel Domains
* 2016: Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels
* 2016: Sketch Me That Shoe
* 2016: survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution, A
* 2017: Attribute-Enhanced Face Recognition with Neural Tensor Fusion Networks
* 2017: Dataset for Persistent Multi-target Multi-camera Tracking in RGB-D, A
* 2017: Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval
* 2017: Deeper, Broader and Artier Domain Generalization
* 2017: Discovery of Shared Semantic Spaces for Multiscene Video Query and Summarization
* 2017: Free-Hand Sketch Synthesis with Deformable Stroke Models
* 2017: Semantic Regularisation for Recurrent Image Annotation
* 2017: Sketch-a-Net: A Deep Neural Network that Beats Humans
* 2017: Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval
* 2017: Transductive Zero-Shot Action Recognition by Word-Vector Embedding
* 2017: Transferring CNNS to multi-instance multi-label classification on small datasets
* 2017: Weakly-Supervised Image Annotation and Segmentation with Objects and Attributes
* 2018: Deep Factorised Inverse-Sketching
* 2018: Deep Multi-task Learning to Recognise Subtle Facial Expressions of Mental States
* 2018: Deep Mutual Learning
* 2018: Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice
* 2018: Frankenstein: Learning Deep Face Representations Using Small Data
* 2018: iVQA: Inverse Visual Question Answering
* 2018: Learning Deep Sketch Abstraction
* 2018: Learning to Compare: Relation Network for Few-Shot Learning
* 2018: Learning to Sketch with Shortcut Cycle Consistency
* 2018: Multi-level Factorisation Net for Person Re-identification
* 2018: Scalable and Effective Deep CCA via Soft Decorrelation
* 2018: Sketch-a-Classifier: Sketch-Based Photo Classifier Generation
* 2018: SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
* 2018: Universal Sketch Perceptual Grouping
* 2019: Episodic Training for Domain Generalization
* 2019: Generalising Fine-Grained Sketch-Based Image Retrieval
* 2019: Generalizable Person Re-Identification by Domain-Invariant Mapping Network
* 2019: Goal-Driven Sequential Data Abstraction
* 2019: Toward Deep Universal Sketch Perceptual Grouper
* 2020: Deep Ranking for Image Zero-Shot Multi-Label Classification
* 2020: Differentiable Automatic Data Augmentation
* 2020: Editorial: Special Issue on Machine Vision with Deep Learning
* 2020: Factorized Higher-Order CNNs With an Application to Spatio-Temporal Emotion Estimation
* 2020: Incremental Few-Shot Object Detection
* 2020: Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool
* 2020: Learning to Generate Novel Domains for Domain Generalization
* 2020: Online Meta-learning for Multi-source and Semi-supervised Domain Adaptation
* 2020: Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval
* 2020: Sketch-a-Segmenter: Sketch-Based Photo Segmenter Generation
* 2020: Solving Mixed-Modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval
* 2020: Unsupervised Batch Normalization
* 2021: Cloud2Curve: Generation and Vectorization of Parametric Sketches
* 2021: Fine-Grained Instance-Level Sketch-Based Image Retrieval
* 2021: Fine-Grained Instance-Level Sketch-Based Video Retrieval
* 2021: How Well Do Self-Supervised Models Transfer?
* 2021: NewtonianVAE: Proportional Control and Goal Identification from Pixels via Physical Latent Spaces
* 2021: On Learning Semantic Representations for Large-Scale Abstract Sketches
* 2021: Searching for Robustness: Loss Learning for Noisy Classification Tasks
* 2021: Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
* 2021: Simple Feature Augmentation for Domain Generalization, A
* 2021: Toward Fine-Grained Sketch-Based 3D Shape Retrieval
* 2021: Vectorization and Rasterization: Self-Supervised Learning for Sketch and Handwriting
* 2022: Meta-Learning in Neural Networks: A Survey
* 2022: Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference
* 2023: Accelerating Self-Supervised Learning via Efficient Training Strategies
* 2023: Deep Learning for Free-Hand Sketch: A Survey
* 2023: Erudite Fine-Grained Visual Classification Model, An
* 2023: Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
* 2023: On-the-Fly Category Discovery
* 2023: Quality Diversity for Visual Pre-Training
* 2023: Task-Aware Adaptive Learning for Cross-domain Few-Shot Learning
* 2023: Uncertainty-Aware Source-Free Domain Adaptive Semantic Segmentation
* 2023: Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style
Includes: Hospedales, T.M. Hospedales, T.M.[Timothy M.] Hospedales, T.M.[Tim M.]
106 for Hospedales, T.M.
Hospedales, T.N.[Timothy N.]
* 2020: BézierSketch: A Generative Model for Scalable Vector Sketches
Hospital, M.
* 1986: Characterization of Some Drilled Pit Images from Contour and Ridge Points Detection
* 1987: 3D Curve Based Matching Method Using Dynamic Programming
* 1987: Pattern Recognition by Traversal of Connected Components of a Line Segments Field
* 1988: Recognition and Positioning of Three-Dimensional Objects by Combining Matchings of Primitive Local Patterns