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Found 13 papers, 8 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.
PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation
To enhance extrinsic pose estimation, a multi-point contact strategy is integrated into the model input, recognizing that identical F/T readings can indicate different poses.
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.
Enhancing Low-resolution Face Recognition with Feature Similarity Knowledge Distillation
This approach differs from conventional knowledge distillation frameworks, which use the L_p distance metrics and offer the advantage of converging well when reducing the distance between features of different resolutions.
Block Selection Method for Using Feature Norm in Out-of-distribution Detection
In this study, we first revealed that a norm of the feature map obtained from the other block than the last block can be a better indicator of OOD detection.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition
Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images.
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.
Multiple Classification with Split Learning
We divided a single deep learning architecture into a common extractor, a cloud model and a local classifier for the distributed learning.
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
