Localization-Aware Meta Tracker Guided With Adversarial Features
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2930550
Abstract: Deep learning has recently shown great potentials in learning powerful features for visual tracking. However, most deep learning-based trackers neglect localization accuracy in the evaluation process of candidates. What’s more, they usually over-rely on the… read more here.
Keywords: meta tracker; localization; adversarial features; aware meta ... See more keywords
MetaSeg: Content-Aware Meta-Net for Omni-Supervised Semantic Segmentation.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3263335
Abstract: Noisy labels, inevitably existing in pseudo-segmentation labels generated from weak object-level annotations, severely hamper model optimization for semantic segmentation. Previous works often rely on massive handcrafted losses and carefully tuned hyperparameters to resist noise, suffering… read more here.
Keywords: content aware; segmentation; model; meta net ... See more keywords
Side-Aware Meta-Learning for Cross-Dataset Listener Diagnosis With Subjective Tinnitus
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2022.3201158
Abstract: With the development of digital technology, machine learning has paved the way for the next generation of tinnitus diagnoses. Although machine learning has been widely applied in EEG-based tinnitus analysis, most current models are dataset-specific.… read more here.
Keywords: meta learning; side aware; cross dataset; dataset ... See more keywords