1.1k citations · 1.8k across the 30 of their papers we have counts for
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SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation
Youssef Dawoud, Gustavo Carneiro, Vasileios Belagiannis
Generalisation of deep neural networks becomes vulnerable when distribution shifts are encountered between train (source) and test (target) domain data. Few-shot domain adaptation…
Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation
Fengbei Liu, Chong Wang, Yuanhong Chen +2
Although noisy-label learning is often approached with discriminative methods for simplicity and speed, generative modeling offers a principled alternative by capturing the joint m…
Multi-modal Learning with Missing Modality via Shared-Specific Feature Modelling
Hu Wang, Yuanhong Chen, Congbo Ma +3
The missing modality issue is critical but non-trivial to be solved by multi-modal models. Current methods aiming to handle the missing modality problem in multi-modal tasks, eithe…
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Deep learning faces a formidable challenge when handling noisy labels, as models tend to overfit samples affected by label noise. This challenge is further compounded by the presen…
Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation
Yuanhong Chen, Yuyuan Liu, Hu Wang +4
Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning cr…
Multi-Head Multi-Loss Model Calibration
Adrian Galdran, Johan Verjans, Gustavo Carneiro +1
Delivering meaningful uncertainty estimates is essential for a successful deployment of machine learning models in the clinical practice. A central aspect of uncertainty quantifica…