56 research outputs found
Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification
Deep learning has been successful for many computer vision tasks due to the
availability of shared and centralised large-scale training data. However,
increasing awareness of privacy concerns poses new challenges to deep learning,
especially for human subject related recognition such as person
re-identification (Re-ID). In this work, we solve the Re-ID problem by
decentralised learning from non-shared private training data distributed at
multiple user sites of independent multi-domain label spaces. We propose a
novel paradigm called Federated Person Re-Identification (FedReID) to construct
a generalisable global model (a central server) by simultaneously learning with
multiple privacy-preserved local models (local clients). Specifically, each
local client receives global model updates from the server and trains a local
model using its local data independent from all the other clients. Then, the
central server aggregates transferrable local model updates to construct a
generalisable global feature embedding model without accessing local data so to
preserve local privacy. This client-server collaborative learning process is
iteratively performed under privacy control, enabling FedReID to realise
decentralised learning without sharing distributed data nor collecting any
centralised data. Extensive experiments on ten Re-ID benchmarks show that
FedReID achieves compelling generalisation performance beyond any locally
trained models without using shared training data, whilst inherently protects
the privacy of each local client. This is uniquely advantageous over
contemporary Re-ID methods
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces
Conventional centralised deep learning paradigms are not feasible when data
from different sources cannot be shared due to data privacy or transmission
limitation. To resolve this problem, federated learning has been introduced to
transfer knowledge across multiple sources (clients) with non-shared data while
optimising a globally generalised central model (server). Existing federated
learning paradigms mostly focus on transferring holistic high-level knowledge
(such as class) across models, which are closely related to specific objects of
interest so may suffer from inverse attack. In contrast, in this work, we
consider transferring mid-level semantic knowledge (such as attribute) which is
not sensitive to specific objects of interest and therefore is more
privacy-preserving and scalable. To this end, we formulate a new Federated
Zero-Shot Learning (FZSL) paradigm to learn mid-level semantic knowledge at
multiple local clients with non-shared local data and cumulatively aggregate a
globally generalised central model for deployment. To improve model
discriminative ability, we propose to explore semantic knowledge augmentation
from external knowledge for enriching the mid-level semantic space in FZSL.
Extensive experiments on five zeroshot learning benchmark datasets validate the
effectiveness of our approach for optimising a generalisable federated learning
model with mid-level semantic knowledge transfer.Comment: Under Revie
Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation: Technical Report for ICCV 2023 OmniObject3D Challenge
In this technical report, we present a solution for 3D object generation of
ICCV 2023 OmniObject3D Challenge. In recent years, 3D object generation has
made great process and achieved promising results, but it remains a challenging
task due to the difficulty of generating complex, textured and high-fidelity
results. To resolve this problem, we study learning effective NeRFs and SDFs
representations with 3D Generative Adversarial Networks (GANs) for 3D object
generation. Specifically, inspired by recent works, we use the efficient
geometry-aware 3D GANs as the backbone incorporating with label embedding and
color mapping, which enables to train the model on different taxonomies
simultaneously. Then, through a decoder, we aggregate the resulting features to
generate Neural Radiance Fields (NeRFs) based representations for rendering
high-fidelity synthetic images. Meanwhile, we optimize Signed Distance
Functions (SDFs) to effectively represent objects with 3D meshes. Besides, we
observe that this model can be effectively trained with only a few images of
each object from a variety of classes, instead of using a great number of
images per object or training one model per class. With this pipeline, we can
optimize an effective model for 3D object generation. This solution is one of
the final top-3-place solutions in the ICCV 2023 OmniObject3D Challenge
Optimisation of the enzyme-linked lectin assay for enhanced glycoprotein and glycoconjugate analysis
Lectin’s are proteins capable of recognising and binding to specific oligosaccharide tructures found on glycoproteins and other biomoloecules. As such they have found tility for glycoanalytical applications. One common difficulty encountered in the pplication of these proteins, particularly in multi-well plate assay formats known as Enzyme Linked Lectin Assays (ELLA’s), is in finding appropriate blocking solutions to prevent non-specific binding with plate surfaces. Many commonly used blocking agents contain carbohydrates and generate significant background signals in ELLA’s, limiting the utility of the assay.
In this study we examined the suitability of a range of blocking reagents, including rotein based, synthetic and commercially available carbohydrate free blocking eagents, for ELLA applications. Each blocking reagent was assessed against a panel f 19 commercially available biotinylated lectins exhibiting diverse structures and arbohydrate specificities. We identified the synthetic polymer Polyvinyl Alcohol PVA) as the best global blocking agent for performing ELLA’s. We ultimately present n ELLA methodology facilitating broad spectrum lectin analysis of glycoconjugates nd extending the utility of the ELLA
Decline in subarachnoid haemorrhage volumes associated with the first wave of the COVID-19 pandemic
BACKGROUND: During the COVID-19 pandemic, decreased volumes of stroke admissions and mechanical thrombectomy were reported. The study\u27s objective was to examine whether subarachnoid haemorrhage (SAH) hospitalisations and ruptured aneurysm coiling interventions demonstrated similar declines.
METHODS: We conducted a cross-sectional, retrospective, observational study across 6 continents, 37 countries and 140 comprehensive stroke centres. Patients with the diagnosis of SAH, aneurysmal SAH, ruptured aneurysm coiling interventions and COVID-19 were identified by prospective aneurysm databases or by International Classification of Diseases, 10th Revision, codes. The 3-month cumulative volume, monthly volumes for SAH hospitalisations and ruptured aneurysm coiling procedures were compared for the period before (1 year and immediately before) and during the pandemic, defined as 1 March-31 May 2020. The prior 1-year control period (1 March-31 May 2019) was obtained to account for seasonal variation.
FINDINGS: There was a significant decline in SAH hospitalisations, with 2044 admissions in the 3 months immediately before and 1585 admissions during the pandemic, representing a relative decline of 22.5% (95% CI -24.3% to -20.7%, p\u3c0.0001). Embolisation of ruptured aneurysms declined with 1170-1035 procedures, respectively, representing an 11.5% (95%CI -13.5% to -9.8%, p=0.002) relative drop. Subgroup analysis was noted for aneurysmal SAH hospitalisation decline from 834 to 626 hospitalisations, a 24.9% relative decline (95% CI -28.0% to -22.1%, p\u3c0.0001). A relative increase in ruptured aneurysm coiling was noted in low coiling volume hospitals of 41.1% (95% CI 32.3% to 50.6%, p=0.008) despite a decrease in SAH admissions in this tertile.
INTERPRETATION: There was a relative decrease in the volume of SAH hospitalisations, aneurysmal SAH hospitalisations and ruptured aneurysm embolisations during the COVID-19 pandemic. These findings in SAH are consistent with a decrease in other emergencies, such as stroke and myocardial infarction
Generalising without Forgetting for Lifelong Person Re-Identification
Existing person re-identification (Re-ID) methods mostly prepare all training data in advance, while real-world Re-ID data are inherently captured over time or from different locations, which requires a model to be incrementally generalised from sequential learning of piecemeal new data without forgetting what is already learned. In this work, we call this lifelong person Re-ID, characterised by solving a problem of unseen class identification subject to continuous new domain generalisation and adaptation with class imbalanced learning. We formulate a new Generalising without Forgetting method (GwFReID) for lifelong Re-ID and design a comprehensive learning objective that accounts for classification coherence, distribution coherence and representation coherence in a unified framework. This design helps to simultaneously learn new information, distil old knowledge and solve class imbalance, which enables GwFReID to incrementally improve model generalisation without catastrophic forgetting of what is already learned. Extensive experiments on eight Re-ID benchmarks, CIFAR-100 and ImageNet show the superiority of GwFReID over the state-of-the-art methods
Tracklet Self-Supervised Learning for Unsupervised Person Re-Identification
Existing unsupervised person re-identification (re-id) methods mainly focus on cross-domain adaptation or one-shot learning. Although they are more scalable than the supervised learning counterparts, relying on a relevant labelled source domain or one labelled tracklet per person initialisation still restricts their scalability in real-world deployments. To alleviate these problems, some recent studies develop unsupervised tracklet association and bottom-up image clustering methods, but they still rely on explicit camera annotation or merely utilise suboptimal global clustering. In this work, we formulate a novel tracklet self-supervised learning (TSSL) method, which is capable of capitalising directly from abundant unlabelled tracklet data, to optimise a feature embedding space for both video and image unsupervised re-id. This is achieved by designing a comprehensive unsupervised learning objective that accounts for tracklet frame coherence, tracklet neighbourhood compactness, and tracklet cluster structure in a unified formulation. As a pure unsupervised learning re-id model, TSSL is end-to-end trainable at the absence of source data annotation, person identity labels, and camera prior knowledge. Extensive experiments demonstrate the superiority of TSSL over a wide variety of the state-of-the-art alternative methods on four large-scale person re-id benchmarks, including Market-1501, DukeMTMC-ReID, MARS and DukeMTMC-VideoReID
The gasdynamic and electromagnetic factors affecting the position of arc roots in a tubular arc heater
A thermal analysis of characteristic parameters in the electrode-region for tubular plasma generators
- …