3 accepted papers
Recent efforts to integrate low-rank adaptation (LoRA) with the Mixture-of-Experts (MoE) have managed to achieve performance comparable to full-parameter fine-tuning by tuning much fewer parameters. Despite promising results, research on improving the efficiency and expert analysis of LoRA with MoE…
The success of data mixing augmentations in image classification tasks has been well-received. However, these techniques cannot be readily applied to object detection due to challenges such as spatial misalignment, foreground/background distinction, and plurality of instances. To tackle these issues…
Crowdsourced 3D CAD models are easily accessible online, and can potentially generate an infinite number of training images for almost any object category. We show that augmenting the training data of contemporary Deep Convolutional Neural Net (DCNN) models with such synthetic data can be effective,…