Search Results for author:
Found 12 papers, 3 papers with code
Rethinking Soft Label in Label Distribution Learning Perspective
The primary goal of training in early convolutional neural networks (CNN) is the higher generalization performance of the model.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Blockwise Temporal-Spatial Pathway Network
We propose a 3D-CNN-based action recognition model, called the blockwise temporal-spatial path-way network (BTSNet), which can adjust the temporal and spatial receptive fields by multiple pathways.
PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?
The improvement between video/kinematic-based methods and the uni-modality ones was significant for all of the teams.
Ranked #1 on
Semantic Segmentation
on PETRAW
Kinematic Based Workflow Recognition
Segmentation Based Workflow Recognition
+4
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary Tasks
The SSKT is independent of the network structure and dataset, and is trained differently from existing knowledge transfer methods; hence, it has an advantage in that the prior knowledge acquired from various tasks can be naturally transferred during the training process to the target task.
hSDB-instrument: Instrument Localization Database for Laparoscopic and Robotic Surgeries
Localization information for all instruments is provided in the form of a bounding box for object detection.
Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations
For gastrectomy for gastric cancer has more complex twenty-one surgical phases, we generate consensus annotation by the revision process with five specialists.
Deep Active Learning with Augmentation-based Consistency Estimation
On the other hand, with the advent of data augmentation metrics as the regularizer on general deep learning, we notice that there can be a mutual influence between the method of unlabeled data selection and the data augmentation-based regularization techniques in active learning scenarios.
Semi-Supervised Object Detection with Sparsely Annotated Dataset
However, when training an anchor-based detectors with sparse annotations on an image, effort to find effective positive examples can hinder training performance.
Development of Fast Refinement Detectors on AI Edge Platforms
With the improvements in the object detection networks, several variations of object detection networks have been achieved impressive performance.
Co-occurrence matrix analysis-based semi-supervised training for object detection
We used an MS-COCO detection dataset to verify the performance of the proposed SSL method and deformable neural networks (D-ConvNets) as an object detector for basic training.
End-to-End Pedestrian Collision Warning System based on a Convolutional Neural Network with Semantic Segmentation
Traditional pedestrian collision warning systems sometimes raise alarms even when there is no danger (e. g., when all pedestrians are walking on the sidewalk).
