Search Results for author:
Found 3 papers, 2 papers with code
SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery
This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue.
Improving Visual Representation Learning through Perceptual Understanding
We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features.
Ranked #2 on
Self-Supervised Image Classification
on ImageNet (finetuned)
(using extra training data)
Representation Learning
Self-Supervised Image Classification
Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation
Furthermore, even with an improved saliency method introduced in this work, we show that qualitative studies may not always correspond with quantitative measures, necessitating the combination of inspection tools in order to provide sufficient insights into the behaviour of trained agents.
