5 citations · 9 across the 7 of their papers we have counts for
7 papers
Early Stopping Against Label Noise Without Validation Data
Suqin Yuan, Lei Feng, Tongliang Liu
Early stopping methods in deep learning face the challenge of balancing the volume of training and validation data, especially in the presence of label noise. Concretely, sparing m…
Enhancing User-Centric Privacy Protection: An Interactive Framework through Diffusion Models and Machine Unlearning
Huaxi Huang, Xin Yuan, Qiyu Liao +2
In the realm of multimedia data analysis, the extensive use of image datasets has escalated concerns over privacy protection within such data. Current research predominantly focuse…
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov +77
Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a sign…
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification
Zhuo Li, He Zhao, Zhen Li +3
Real-world datasets usually are class-imbalanced and corrupted by label noise. To solve the joint issue of long-tailed distribution and label noise, most previous works usually aim…
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning
Qiang Qu, Yiran Shen, Xiaoming Chen +2
The bio-inspired event cameras or dynamic vision sensors are capable of asynchronously capturing per-pixel brightness changes (called event-streams) in high temporal resolution and…
Prompt-based Multi-interest Learning Method for Sequential Recommendation
Xue Dong, Xuemeng Song, Tongliang Liu +1
Multi-interest learning method for sequential recommendation aims to predict the next item according to user multi-faceted interests given the user historical interactions. Existin…
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
Runnan Chen, Xinge Zhu, Nenglun Chen +6
Current successful methods of 3D scene perception rely on the large-scale annotated point cloud, which is tedious and expensive to acquire. In this paper, we propose Model2Scene, a…