The Lipschitz bound, a technique from robust statistics, can limit the
m...
Current mainstream vision-language (VL) tracking framework consists of t...
Artificial intelligence (AI) is evolving towards artificial general
inte...
Well-annotated medical datasets enable deep neural networks (DNNs) to ga...
Graph neural networks (GNNs) have shown remarkable performance on divers...
The self-supervised ultrasound (US) video model pretraining can use a sm...
Even pruned by the state-of-the-art network compression methods, Graph N...
Prevailing deep graph learning models often suffer from label sparsity i...
Graph contrastive learning (GCL) is prevalent to tackle the supervision
...
Generative self-supervised learning (SSL), especially masked autoencoder...
Recently, contrastiveness-based augmentation surges a new climax in the
...
Neural networks often encounter various stringent resource constraints w...
In this work, we contribute a new million-scale Unmanned Aerial Vehicle ...
Knowledge distillation provides an effective way to transfer knowledge v...
Most deep neural networks (DNNs) based ultrasound (US) medical image ana...
To prevent the leakage of private information while enabling automated
m...