85 citations · 90 across the 5 of their papers we have counts for
7 papers
Deep Unsupervised Key Frame Extraction for Efficient Video Classification
Hao Tang, Lei Ding, Songsong Wu +3
Video processing and analysis have become an urgent task since a huge amount of videos (e.g., Youtube, Hulu) are uploaded online every day. The extraction of representative key fra…
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
Alessandro Conti, Paolo Rota, Yiming Wang +1
Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding. While models devised for Facial Expression Recognition (FER) have dem…
Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks wh…
Continual Attentive Fusion for Incremental Learning in Semantic Segmentation
Guanglei Yang, Enrico Fini, Dan Xu +5
Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved perform…
Low-Budget Label Query through Domain Alignment Enforcement
Jurandy Almeida, Cristiano Saltori, Paolo Rota +1
Deep learning revolution happened thanks to the availability of a massive amount of labelled data which have contributed to the development of models with extraordinary inference c…
Curriculum Self-Paced Learning for Cross-Domain Object Detection
Petru Soviany, Radu Tudor Ionescu, Paolo Rota +1
Training (source) domain bias affects state-of-the-art object detectors, such as Faster R-CNN, when applied to new (target) domains. To alleviate this problem, researchers proposed…