no code implementations • 17 Sep 2014 • Shuxin Ouyang, Timothy Hospedales, Yi-Zhe Song, Xueming Li
Heterogeneous face recognition (HFR) refers to matching face imagery across different domains.
2 code implementations • 30 Jan 2015 • Qian Yu, Yongxin Yang, Yi-Zhe Song, Tao Xiang, Timothy Hospedales
We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans.
no code implementations • 5 Feb 2015 • Xun Xu, Timothy Hospedales, Shaogang Gong
In this framework a mapping is constructed between visual features and a human interpretable semantic description of each category, allowing categories to be recognised in the absence of any training data.
Ranked #31 on Zero-Shot Action Recognition on UCF101
no code implementations • 9 Apr 2015 • Guosheng Hu, Yongxin Yang, Dong Yi, Josef Kittler, William Christmas, Stan Z. Li, Timothy Hospedales
In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible.
no code implementations • CVPR 2015 • Yonggang Qi, Yi-Zhe Song, Tao Xiang, Honggang Zhang, Timothy Hospedales, Yi Li, Jun Guo
We propose a perceptual grouping framework that organizes image edges into meaningful structures and demonstrate its usefulness on various computer vision tasks.
no code implementations • 27 Jul 2015 • Xun Xu, Timothy Hospedales, Shaogang Gong
The growing rate of public space CCTV installations has generated a need for automated methods for exploiting video surveillance data including scene understanding, query, behaviour annotation and summarization.
no code implementations • 28 Jul 2015 • Yongxin Yang, Timothy Hospedales
Most visual recognition methods implicitly assume the data distribution remains unchanged from training to testing.
no code implementations • 9 Oct 2015 • Yi Li, Yi-Zhe Song, Timothy Hospedales, Shaogang Gong
We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category.
no code implementations • 13 Nov 2015 • Xun Xu, Timothy Hospedales, Shaogang Gong
This is a more challenging problem than existing ZSL of still images and/or attributes, because the mapping between video spacetime features of actions and the semantic space is more complex and harder to learn for the purpose of generalising over any cross-category domain shift.
no code implementations • 21 Mar 2016 • Guosheng Hu, Xiaojiang Peng, Yongxin Yang, Timothy Hospedales, Jakob Verbeek
To train such networks, very large training sets are needed with millions of labeled images.
2 code implementations • 20 May 2016 • Yongxin Yang, Timothy Hospedales
Our approach applies to both homogeneous and heterogeneous MTL.
no code implementations • 29 Jun 2017 • Flood Sung, Li Zhang, Tao Xiang, Timothy Hospedales, Yongxin Yang
We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples.
1 code implementation • 12 Sep 2017 • Guosheng Hu, Yuxin Hu, Kai Yang, Zehao Yu, Flood Sung, Zhihong Zhang, Fei Xie, Jianguo Liu, Neil Robertson, Timothy Hospedales, Qiangwei Miemie
We propose a novel investment decision strategy (IDS) based on deep learning.
Computational Finance
no code implementations • CVPR 2018 • Jifei Song, Kaiyue Pang, Yi-Zhe Song, Tao Xiang, Timothy Hospedales
In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process.
no code implementations • NeurIPS 2019 • Carl Allen, Ivana Balažević, Timothy Hospedales
We show that different interactions between PMI vectors reflect semantic word relationships, such as similarity and paraphrasing, that are encoded in low dimensional word embeddings under a suitable projection, theoretically explaining why embeddings of W2V and GloVe work.
no code implementations • ICLR 2018 • Kunkun Pang, Mingzhi Dong, Yang Wu, Timothy Hospedales
In contrast to this body of research, we propose to treat active learning algorithm design as a meta-learning problem and learn the best criterion from data.
no code implementations • ECCV 2018 • Guosheng Hu, Li Liu, Yang Yuan, Zehao Yu, Yang Hua, Zhihong Zhang, Fumin Shen, Ling Shao, Timothy Hospedales, Neil Robertson, Yongxin Yang
To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild database (LSEMSW).
no code implementations • EMNLP 2018 • Tanmoy Mukherjee, Makoto Yamada, Timothy Hospedales
Word translation, or bilingual dictionary induction, is an important capability that impacts many multilingual language processing tasks.
no code implementations • 7 Nov 2018 • Marija Jegorova, Stéphane Doncieux, Timothy Hospedales
Leveraging our generative policy network, a robot can sample novel behaviors until it finds one that works for a new environment.
no code implementations • 28 Jan 2019 • Carl Allen, Timothy Hospedales
Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e. g. the embeddings of analogy "woman is to queen as man is to king" approximately describe a parallelogram.
1 code implementation • NeurIPS 2019 • Ivana Balažević, Carl Allen, Timothy Hospedales
Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean analogues.
Ranked #38 on Link Prediction on WN18RR
1 code implementation • ICLR 2020 • Miguel Jaques, Michael Burke, Timothy Hospedales
Our approach significantly outperforms related unsupervised methods in long-term future frame prediction of systems with interacting objects (such as ball-spring or 3-body gravitational systems), due to its ability to build dynamics into the model as an inductive bias.
no code implementations • CVPR 2020 • Jean Kossaifi, Antoine Toisoul, Adrian Bulat, Yannis Panagakis, Timothy Hospedales, Maja Pantic
To alleviate this, one approach is to apply low-rank tensor decompositions to convolution kernels in order to compress the network and reduce its number of parameters.
no code implementations • 16 Aug 2019 • Xun Xu, Shaogang Gong, Timothy Hospedales
To that end, we relax the common assumption that each individual crowd video instance is only associated with a single crowd attribute.
no code implementations • 25 Sep 2019 • Tianyuan Yu, Yongxin Yang, Da Li, Timothy Hospedales, Tao Xiang
Stochastic neural networks (SNNs) are currently topical, with several paradigms being actively investigated including dropout, Bayesian neural networks, variational information bottleneck (VIB) and noise regularized learning.
no code implementations • ICLR 2021 • Carl Allen, Ivana Balažević, Timothy Hospedales
Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be inferred.
no code implementations • 15 Oct 2019 • Marija Jegorova, Antti Ilari Karjalainen, Jose Vazquez, Timothy Hospedales
High-quality realistic sonar data simulation could be of benefit to multiple applications, including training of human operators for post-mission analysis, as well as tuning and validation of autonomous target recognition (ATR) systems for underwater vehicles.
1 code implementation • 2 Mar 2020 • Marija Jegorova, Joshua Smith, Michael Mistry, Timothy Hospedales
Dynamic System Identification approaches usually heavily rely on the evolutionary and gradient-based optimisation techniques to produce optimal excitation trajectories for determining the physical parameters of robot platforms.
1 code implementation • ECCV 2020 • Yonggang Li, Guosheng Hu, Yongtao Wang, Timothy Hospedales, Neil M. Robertson, Yongxin Yang
In this paper, we propose Differentiable Automatic Data Augmentation (DADA) which dramatically reduces the cost.
Ranked #15 on Data Augmentation on ImageNet
no code implementations • CVPR 2020 • Juan-Manuel Perez-Rua, Xiatian Zhu, Timothy Hospedales, Tao Xiang
To this end we propose OpeN-ended Centre nEt (ONCE), a detector designed for incrementally learning to detect novel class objects with few examples.
no code implementations • 12 Mar 2020 • Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, Tao Xiang
This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier.
Ranked #63 on Domain Generalization on PACS
no code implementations • 3 Apr 2020 • Da Li, Yongxin Yang, Yi-Zhe Song, Timothy Hospedales
In DG this means encountering a sequence of domains and at each step training to maximise performance on the next domain.
Ranked #76 on Domain Generalization on PACS
no code implementations • ECCV 2020 • Da Li, Timothy Hospedales
Therefore we propose an online shortest-path meta-learning framework that is both computationally tractable and practically effective for improving DA performance.
Meta-Learning Multi-Source Unsupervised Domain Adaptation +2
1 code implementation • 11 Apr 2020 • Timothy Hospedales, Antreas Antoniou, Paul Micaelli, Amos Storkey
We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning.
1 code implementation • 11 May 2020 • Xinwang Liu, En Zhu, Jiyuan Liu, Timothy Hospedales, Yang Wang, Meng Wang
We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM).
no code implementations • 13 May 2020 • Stephane Doncieux, Nicolas Bredeche, Léni Le Goff, Benoît Girard, Alexandre Coninx, Olivier Sigaud, Mehdi Khamassi, Natalia Díaz-Rodríguez, David Filliat, Timothy Hospedales, A. Eiben, Richard Duro
Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors.
1 code implementation • 26 May 2020 • Shreyank N Gowda, Panagiotis Eustratiadis, Timothy Hospedales, Laura Sevilla-Lara
We treat this as a grouping problem by exploiting object proposals and making a joint inference about grouping over both space and time.
no code implementations • 1 Jun 2020 • Lucas Deecke, Timothy Hospedales, Hakan Bilen
While recent techniques in domain adaptation and multi-domain learning enable the learning of more domain-agnostic features, their success relies on the presence of domain labels, typically requiring manual annotation and careful curation of datasets.
no code implementations • CVPR 2021 • Miguel Jaques, Michael Burke, Timothy Hospedales
Learning low-dimensional latent state space dynamics models has been a powerful paradigm for enabling vision-based planning and learning for control.
no code implementations • 10 Jun 2020 • Carl Allen, Ivana Balažević, Timothy Hospedales
Much progress has been made in semi-supervised learning (SSL) by combining methods that exploit different aspects of the data distribution, e. g. consistency regularisation relies on properties of $p(x)$, whereas entropy minimisation pertains to the label distribution $p(y|x)$.
2 code implementations • 15 Jun 2020 • Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
In particular, we study the problem of label distillation - creating synthetic labels for a small set of real images, and show it to be more effective than the prior image-based approach to dataset distillation.
1 code implementation • ECCV 2020 • Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
The study of neural generative models of human sketches is a fascinating contemporary modeling problem due to the links between sketch image generation and the human drawing process.
1 code implementation • 6 Jul 2020 • Ivana Balažević, Carl Allen, Timothy Hospedales
In this work, we propose a probabilistically principled general approach to SSL that considers the distribution over label predictions, for labels of different complexity, from "one-hot" vectors to binary vectors and images.
1 code implementation • ECCV 2020 • Kaiyang Zhou, Yongxin Yang, Timothy Hospedales, Tao Xiang
This explicitly increases the diversity of available training domains and leads to a more generalizable model.
Ranked #65 on Domain Generalization on PACS
1 code implementation • 17 Oct 2020 • Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales
Stochastic Neural Networks (SNNs) that inject noise into their hidden layers have recently been shown to achieve strong robustness against adversarial attacks.
no code implementations • 2 Dec 2020 • Jiechao Guan, Zhiwu Lu, Tao Xiang, Timothy Hospedales
By transferring knowledge learned from seen/previous tasks, meta learning aims to generalize well to unseen/future tasks.
no code implementations • 9 Dec 2020 • Chenyang Zhao, Timothy Hospedales
In reinforcement learning, domain randomisation is an increasingly popular technique for learning more general policies that are robust to domain-shifts at deployment.
no code implementations • 1 Jan 2021 • Lucas Deecke, Timothy Hospedales, Hakan Bilen
A fundamental shortcoming of deep neural networks is their specialization to a single task and domain.
2 code implementations • ICCV 2021 • Xueting Zhang, Debin Meng, Henry Gouk, Timothy Hospedales
Current state-of-the-art few-shot learners focus on developing effective training procedures for feature representations, before using simple, e. g. nearest centroid, classifiers.
Ranked #6 on Few-Shot Image Classification on Meta-Dataset
cross-domain few-shot learning Few-Shot Image Classification
1 code implementation • CVPR 2021 • Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations.
1 code implementation • 17 Jun 2021 • Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
The problem is especially noticeable when using modern neural networks, for which there is a significant difference between the confidence of the model and the probability of correct prediction.
1 code implementation • NeurIPS 2021 • Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters.
1 code implementation • 15 Jul 2021 • Rui Li, Ondrej Bohdal, Rajesh Mishra, Hyeji Kim, Da Li, Nicholas Lane, Timothy Hospedales
We use our MetaCC benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift, which can be controlled in the coding problem.
no code implementations • 13 Sep 2021 • Miguel Jaques, Martin Asenov, Michael Burke, Timothy Hospedales
This paper introduces V-SysId, a novel method that enables simultaneous keypoint discovery, 3D system identification, and extrinsic camera calibration from an unlabeled video taken from a static camera, using only the family of equations of motion of the object of interest as weak supervision.
no code implementations • ICLR 2022 • Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
Learning meaningful representations for chirographic drawing data such as sketches, handwriting, and flowcharts is a gateway for understanding and emulating human creative expression.
no code implementations • 29 Sep 2021 • Boyan Gao, Henry Gouk, Yongxin Yang, Timothy Hospedales
We take a different approach, and explore the impact of the ERM loss function on out-of-domain generalisation.
no code implementations • ICLR 2022 • Lucas Deecke, Timothy Hospedales, Hakan Bilen
A fundamental shortcoming of deep neural networks is their specialization to a single task and domain.
no code implementations • ICLR 2022 • Hae Beom Lee, Hayeon Lee, Jaewoong Shin, Eunho Yang, Timothy Hospedales, Sung Ju Hwang
Many gradient-based meta-learning methods assume a set of parameters that do not participate in inner-optimization, which can be considered as hyperparameters.
no code implementations • 26 Oct 2021 • Adrian Bulat, Jean Kossaifi, Sourav Bhattacharya, Yannis Panagakis, Timothy Hospedales, Georgios Tzimiropoulos, Nicholas D Lane, Maja Pantic
We propose defensive tensorization, an adversarial defence technique that leverages a latent high-order factorization of the network.
no code implementations • 9 Nov 2021 • Minyoung Kim, Timothy Hospedales
In essence, the MAP solution is approximated by the LDA estimate, but to take the GP prior into account, we adopt the prior-norm adjustment to estimate LDA's shared variance parameters, which ensures that the adjusted estimate is consistent with the GP prior.
no code implementations • 1 Feb 2022 • Da Li, Henry Gouk, Timothy Hospedales
However much of the work in general purpose DG is heuristically motivated, as the DG problem is hard to model formally; and recent evaluations have cast doubt on existing methods' practical efficacy -- in particular compared to a well tuned empirical risk minimisation baseline.
1 code implementation • 5 Apr 2022 • Calum Heggan, Sam Budgett, Timothy Hospedales, Mehrdad Yaghoobi
Currently available benchmarks for few-shot learning (machine learning with few training examples) are limited in the domains they cover, primarily focusing on image classification.
Ranked #1 on Few-Shot Audio Classification on NSynth
no code implementations • 15 Jun 2022 • Adrian El Baz, Ihsan Ullah, Edesio Alcobaça, André C. P. L. F. Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Chaoyu Guan, Isabelle Guyon, Timothy Hospedales, Shell Hu, Mike Huisman, Frank Hutter, Zhengying Liu, Felix Mohr, Ekrem Öztürk, Jan N. van Rijn, Haozhe Sun, Xin Wang, Wenwu Zhu
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are available.
no code implementations • 15 Jul 2022 • Ondrej Bohdal, Da Li, Shell Xu Hu, Timothy Hospedales
Recognizing that device's data are likely to come from multiple latent domains that include a mixture of unlabelled domain-relevant and domain-irrelevant examples, we focus on the comparatively under-studied problem of latent domain adaptation.
no code implementations • 17 Jul 2022 • Ruchika Chavhan, Henry Gouk, Jan Stühmer, Timothy Hospedales
Providing invariances in a given learning task conveys a key inductive bias that can lead to sample-efficient learning and good generalisation, if correctly specified.
1 code implementation • 1 Aug 2022 • Panagiotis Eustratiadis, Henry Gouk, Da Li, Timothy Hospedales
This paper investigates a family of methods for defending against adversarial attacks that owe part of their success to creating a noisy, discontinuous, or otherwise rugged loss landscape that adversaries find difficult to navigate.
1 code implementation • 4 Oct 2022 • Yongshuo Zong, Yongxin Yang, Timothy Hospedales
In this work, we introduce MEDFAIR, a framework to benchmark the fairness of machine learning models for medical imaging.
no code implementations • 8 Dec 2022 • Zicheng Liu, Da Li, Javier Fernandez-Marques, Stefanos Laskaridis, Yan Gao, Łukasz Dudziak, Stan Z. Li, Shell Xu Hu, Timothy Hospedales
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities.
1 code implementation • 15 Dec 2022 • Royson Lee, Rui Li, Stylianos I. Venieris, Timothy Hospedales, Ferenc Huszár, Nicholas D. Lane
Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images.
no code implementations • ICCV 2023 • Yurong Guo, Ruoyi Du, Yuan Dong, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
In this paper, we first observe the dependence of task-specific parameter configuration on the target task.
no code implementations • CVPR 2023 • Dongliang Chang, Yujun Tong, Ruoyi Du, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
Therefore, we first propose a feature disentanglement module and a feature re-fusion module to reduce negative transfer and boost positive transfer between different datasets.
1 code implementation • CVPR 2023 • Ruoyi Du, Dongliang Chang, Kongming Liang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
Our code is available at https://github. com/PRIS-CV/On-the-fly-Category-Discovery.
no code implementations • ICCV 2023 • Ruchika Chavhan, Henry Gouk, Da Li, Timothy Hospedales
Notably, the augmentations used in both supervised and self-supervised training lead to features with high invariance to spatial and appearance transformations.
no code implementations • 23 Feb 2023 • Minyoung Kim, Da Li, Timothy Hospedales
We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case target domain and adapt a model to that worst-case domain, thereby improving the model's robustness.
1 code implementation • 24 Feb 2023 • Ruchika Chavhan, Henry Gouk, Jan Stuehmer, Calum Heggan, Mehrdad Yaghoobi, Timothy Hospedales
Contrastive self-supervised learning methods famously produce high quality transferable representations by learning invariances to different data augmentations.
1 code implementation • CVPR 2023 • Fengyin Lin, Mingkang Li, Da Li, Timothy Hospedales, Yi-Zhe Song, Yonggang Qi
This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-category, intra-category, and cross datasets) of ZS-SBIR with just one network (``everything''), and (ii) we would really like to understand how this sketch-photo matching operates (``explainable'').
1 code implementation • 31 Mar 2023 • Yongshuo Zong, Oisin Mac Aodha, Timothy Hospedales
In this survey, we provide a comprehensive review of the state-of-the-art in SSML, in which we elucidate three major challenges intrinsic to self-supervised learning with multimodal data: (1) learning representations from multimodal data without labels, (2) fusion of different modalities, and (3) learning with unaligned data.
no code implementations • 7 Apr 2023 • Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song
Such strictly-ordered discrete factorization however falls short of capturing key properties of chirographic data -- it fails to build holistic understanding of the temporal concept due to one-way visibility (causality).
no code implementations • 16 Apr 2023 • Ondrej Bohdal, Timothy Hospedales, Philip H. S. Torr, Fazl Barez
Successful deployment of artificial intelligence (AI) in various settings has led to numerous positive outcomes for individuals and society.
no code implementations • 8 May 2023 • Minyoung Kim, Timothy Hospedales
We propose a novel hierarchical Bayesian approach to Federated Learning (FL), where our model reasonably describes the generative process of clients' local data via hierarchical Bayesian modeling: constituting random variables of local models for clients that are governed by a higher-level global variate.
1 code implementation • CVPR 2023 • Ondrej Bohdal, Yinbing Tian, Yongshuo Zong, Ruchika Chavhan, Da Li, Henry Gouk, Li Guo, Timothy Hospedales
Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction.
no code implementations • 14 May 2023 • Raman Dutt, Linus Ericsson, Pedro Sanchez, Sotirios A. Tsaftaris, Timothy Hospedales
We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT) techniques for diverse medical image analysis tasks.
1 code implementation • 15 Jun 2023 • Panagiotis Eustratiadis, Łukasz Dudziak, Da Li, Timothy Hospedales
In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes.
1 code implementation • 16 Jun 2023 • Minyoung Kim, Timothy Hospedales
We propose a novel hierarchical Bayesian model for learning with a large (possibly infinite) number of tasks/episodes, which suits well the few-shot meta learning problem.
1 code implementation • 30 Jun 2023 • Martin Ferianc, Ondrej Bohdal, Timothy Hospedales, Miguel Rodrigues
Enhancing the generalisation abilities of neural networks (NNs) through integrating noise such as MixUp or Dropout during training has emerged as a powerful and adaptable technique.
no code implementations • 6 Jul 2023 • Luísa Shimabucoro, Timothy Hospedales, Henry Gouk
Numerous benchmarks for Few-Shot Learning have been proposed in the last decade.
no code implementations • 20 Jul 2023 • Ondrej Bohdal, Da Li, Timothy Hospedales
Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data.
no code implementations • 20 Jul 2023 • Ondrej Bohdal, Da Li, Timothy Hospedales
Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain.
2 code implementations • 22 Sep 2023 • Minyoung Kim, Timothy Hospedales
We release a new Bayesian neural network library for PyTorch for large-scale deep networks.
1 code implementation • 2 Oct 2023 • Yongshuo Zong, Tingyang Yu, Bingchen Zhao, Ruchika Chavhan, Timothy Hospedales
Large language and vision-language models are rapidly being deployed in practice thanks to their impressive capabilities in instruction following, in-context learning, and so on.
1 code implementation • 8 Oct 2023 • Raman Dutt, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy Hospedales
We demonstrate empirically that FairTune leads to improved fairness on a range of medical imaging datasets.
no code implementations • 27 Oct 2023 • Fady Rezk, Antreas Antoniou, Henry Gouk, Timothy Hospedales
We analyze VeLO (versatile learned optimizer), the largest scale attempt to train a general purpose "foundational" optimizer to date.
no code implementations • 13 Nov 2023 • Ruolin Yang, Da Li, Conghui Hu, Timothy Hospedales, Honggang Zhang, Yi-Zhe Song
Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a language expression or a photo mask.
1 code implementation • 24 Nov 2023 • Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma
High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but, due to the enormous capital investment required for training, it is increasingly centralised to a few large corporations, and hidden behind paywalls.
no code implementations • 2 Feb 2024 • Calum Heggan, Sam Budgett, Timothy Hospedales, Mehrdad Yaghoobi
In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data.
1 code implementation • 3 Feb 2024 • Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, Yongxin Yang, Timothy Hospedales
Our experiments demonstrate that integrating this dataset into standard vision-language fine-tuning or utilizing it for post-hoc fine-tuning effectively safety aligns VLLMs.
no code implementations • 14 Mar 2024 • Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Tao Xiang, Timothy Hospedales, Yi-Zhe Song
(ii) SketchINR's auto-decoder provides a much higher-fidelity representation than other learned vector sketch representations, and is uniquely able to scale to complex vector sketches such as FS-COCO.
1 code implementation • 19 Mar 2024 • Yongshuo Zong, Ondrej Bohdal, Timothy Hospedales
Built on top of LLMs, vision large language models (VLLMs) have advanced significantly in areas such as recognition, reasoning, and grounding.