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Found 8 papers, 5 papers with code
Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation
After blocks are adjusted for current test domain, we generate pseudo-labels by averaging given test images and corresponding flipped counterparts.
INSTA-BEEER: Explicit Error Estimation and Refinement for Fast and Accurate Unseen Object Instance Segmentation
Efficient and accurate segmentation of unseen objects is crucial for robotic manipulation.
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
Conventionally, learning-based automatic sleep scoring on single-channel electroencephalogram (EEG) is actively studied because obtaining multi-channel signals during sleep is difficult.
Ranked #1 on
Sleep Stage Detection
on Sleep-EDFx
Automatic Detection of Injection and Press Mold Parts on 2D Drawing Using Deep Neural Network
This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network.
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment.
Object Detection for Understanding Assembly Instruction Using Context-aware Data Augmentation and Cascade Mask R-CNN
Understanding assembly instruction has the potential to enhance the robot s task planning ability and enables advanced robotic applications.
Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data
Segmentation of unseen industrial parts is essential for autonomous industrial systems.
Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG
A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring.
Ranked #1 on
Sleep Stage Detection
on MASS SS2
