Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a task-specific prompt in each input instance during the model training stage.
Recent parameter-efficient language model tuning (PELT) methods manage to match the performance of fine-tuning with much fewer trainable parameters and perform especially well when training data is limited.
Second, segmentation and classification models are connected with two different feature aggregation strategies to enhance the classification performance.
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials & Methods: This retrospective study included a total of 12, 092 patients (mean age 57 +- 18; 6, 172 women) for model development and testing (from 2012-2017).
Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions.
It is appealing to make use of surveillance cameras and to extract user-related information through computer vision.
Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution.
Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing a large fraction of such online sources.
Convolutional Neural Network (CNN) based image segmentation has made great progress in recent years.
Ranked #50 on Semi-Supervised Video Object Segmentation on DAVIS 2016
A video is first divided into equal length clips and next for each clip a set of tube proposals are generated based on 3D CNN features.
A video is first divided into equal length clips and for each clip a set of tube proposals are generated next based on 3D Convolutional Network (ConvNet) features.
Ranked #3 on Action Detection on UCF101-24