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Found 8 papers, 0 papers with code
Future Predictive Success-or-Failure Classification for Long-Horizon Robotic Tasks
The key idea behind the proposed method is an end-to-end approach for determining whether the action plan can complete a given task instead of manually redesigning the conditions.
One-Shot Machine Unlearning with Mnemonic Code
A naive MU approach is to re-train the whole model with the training data from which the undesirable data has been removed.
Multi-Modal Pedestrian Detection with Large Misalignment Based on Modal-Wise Regression and Multi-Modal IoU
Due to this assumption's breakdown, the position of the bounding boxes does not match between the two modalities, resulting in a significant decrease in detection accuracy, especially in regions where the amount of misalignment is large.
Geometric Data Augmentation Based on Feature Map Ensemble
The performance of CNN is dramatically degraded by geometric transformation, such as large rotations.
Generalized Domain Adaptation
The key to our approach is self-supervised class-destructive learning, which enables the learning of class-invariant representations and domain-adversarial classifiers without using any domain labels.
Gradient-Based Low-Light Image Enhancement
A low-light image enhancement is a highly demanded image processing technique, especially for consumer digital cameras and cameras on mobile phones.
Misalignment-Robust Joint Filter for Cross-Modal Image Pairs
Next, the joint-filter cost volume and a set of filtered images are computed from the target image and the set of the translated guidances.
Gradient-Domain Image Reconstruction Framework With Intensity-Range and Base-Structure Constraints
To generate detail-preserving and artifact-free output images, we combine the benefits of the two approaches into the proposed framework by introducing the intensity-range constraint and the base-structure constraint.
