Search Results for author:
Found 6 papers, 1 papers with code
3D Hand Reconstruction via Aggregating Intra and Inter Graphs Guided by Prior Knowledge for Hand-Object Interaction Scenario
To overcome these issues, we propose a 3D hand reconstruction network combining the benefits of model-based and model-free approaches to balance accuracy and physical plausibility for hand-object interaction scenario.
Ranked #3 on
3D Hand Pose Estimation
on HO-3D
Novel Fundus Image Preprocessing for Retcam Images to Improve Deep Learning Classification of Retinopathy of Prematurity
This paper proposes the use of improved novel fundus preprocessing methods using pretrained transfer learning frameworks to create hybrid models to give higher diagnosis accuracy.
SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels
We look deeper into the memorization behavior in training with noisy labels and observe that the network outputs are reliable in the early stage.
Confidence Adaptive Regularization for Deep Learning with Noisy Labels
Recent studies on the memorization effects of deep neural networks on noisy labels show that the networks first fit the correctly-labeled training samples before memorizing the mislabeled samples.
Ranked #28 on
Image Classification
on mini WebVision 1.0
Co-matching: Combating Noisy Labels by Augmentation Anchoring
We then update two networks simultaneously by selecting small-loss instances to minimize both unsupervised matching loss (i. e., measure the consistency of the two networks) and supervised classification loss (i. e. measure the classification performance).
CLTA: Contents and Length-based Temporal Attention for Few-shot Action Recognition
Few-shot action recognition has attracted increasing attention due to the difficulty in acquiring the properly labelled training samples.
