In this paper, we propose a self-supervised approach for tumor segmentation.
The core of MST-GNN is a multiscale spatio-temporal graph that explicitly models the relations in motions at various spatial and temporal scales.
A novel Multi-teacher Single-student Knowledge Distillation (MS-KD) framework is proposed, where the teacher models are pre-trained single-organ segmentation networks, and the student model is a multi-organ segmentation network.
Modern deep neural networks suffer from performance degradation when evaluated on testing data under different distributions from training data.
Besides, we also report the hand and object pose errors with existing baselines and show that the dataset can serve as the video demonstrations for robot imitation learning on the handover task.
Deep neural networks (DNNs) have the capacity to fit extremely noisy labels nonetheless they tend to learn data with clean labels first and then memorize those with noisy labels.
Single-frame temporal action localization (STAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance.
To motivate a wide investigation in such settings, we present a real-world fine-grained domain adaptation task in machine translation (FDMT).
Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance.
Ranked #2 on Weakly Supervised Action Localization on THUMOS 2014
Knowledge distillation is employed to transfer the privileged information from the offline teacher to the online student.
We demonstrate the effectiveness of our methods on two downstream tasks: i) Brain tumor segmentation, ii) Pancreas tumor segmentation.