4 accepted papers
This paper presents Fosafer’s approach to the Track 2 Mandarin in the Multimodal Emotion and Intent Joint Understanding (MEIJU) challenge, which focuses on achieving joint recognition of emotion and intent in Mandarin, despite the issue of category imbalance. To alleviate this issue, we use a variet…
Deep learning-based 3D object detectors often require large-scale labeled 3D datasets, which can be expensive to annotate. To tackle this issue, we introduce a core-set sampling strategy within an active learning framework, selecting highly informative data from a data pool to reduce reliance on suc…
Due to the significant intra-class variance of 3D point clouds, it becomes challenging to characterize prototype features with a small number of instances in few-shot classification. The significant feature discrepancies among instances also hinder category determination. In this paper, we propose a…
3D hand pose estimation has made significant progress recently, where Convolutional Neural Networks (CNNs) play a critical role. However, most of the existing CNN-based hand pose estimation methods depend much on the training set, while labeling 3D hand pose on training data is laborious and time-co…