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Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)
Hoang Son Le, Rini Akmeliawati, Gustavo Carneiro
Domain generalisation (DG) methods address the problem of domain shift, when there is a mismatch between the distributions of training and target domains. Data augmentation approac…
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?
Filipe R. Cordeiro, Gustavo Carneiro
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled by non-specialist annotators, or even specialists in a challenging task, such a…
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Ragav Sachdeva, Filipe R. Cordeiro, Vasileios Belagiannis +2
The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such lar…
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
Binh X. Nguyen, Binh D. Nguyen, Gustavo Carneiro +3
Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample similarities in the embedding space from an unlabeled dataset. Traditional UDML methods usually use the tripl…
Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy
Fengbei Liu, Yaqub Jonmohamadi, Gabriel Maicas +2
Intra-operative automatic semantic segmentation of knee joint structures can assist surgeons during knee arthroscopy in terms of situational awareness. However, due to poor imaging…
Few-Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Julia Hornauer, Gustavo Carneiro +1
Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not…
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu +3
Anomaly detection methods generally target the learning of a normal image distribution (i.e., inliers showing healthy cases) and during testing, samples relatively far from the lea…
Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
Renato Hermoza, Gabriel Maicas, Jacinto C. Nascimento +1
The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classific…
Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume
Adrian Johnston, Gustavo Carneiro
Monocular depth estimation has become one of the most studied applications in computer vision, where the most accurate approaches are based on fully supervised learning models. How…
PAC-Bayes meta-learning with implicit task-specific posteriors
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning. Our proposed method extends the PAC-Bayes framework from a single task…