6 accepted papers
Accurate object detection depends on the precise refinement of bounding box regression. Recent advancements in bounding box regression have introduced a variety of methodologies aimed at reducing the disparity between predicted and ground truth bounding boxes. The prevailing objective functions for
Domain-Adaptive Pre-training (DAP) has recently gained attention for its effectiveness in fine-tuning pre-trained models. Building on this, continual DAP has been explored to develop pre-trained models capable of incrementally incorporating different domain datasets. However, existing continual DAP…
High Dynamic Range (HDR) imaging seeks to enhance image quality by combining multiple Low Dynamic Range (LDR) images captured at varying exposure levels. Traditional deep learning approaches often employ reconstruction loss, but this method can lead to ambiguities in feature space during training. T…
We consider a critical issue of false negatives in Vision- Language Pre-training (VLP) a challenge that arises from the inherent many-to-many correspondence of image-text pairs in large-scale web-crawled datasets. The presence of false negatives can impede achieving optimal performance and even lead…
Despite of pre-processing the raw point cloud is important, limited research has been conducted on learning-based approaches to point cloud upsampling. PU-EdgeFormer [1] model stands out for its exceptional performance, which is attributed to its unique inductive biases that seamlessly blend both lo…
Despite the recent development of deep learning-based point cloud upsampling, most MLP-based point cloud upsampling methods have limitations in that it is difficult to train the local and global structure of the point cloud at the same time. To solve this problem, we present a combined graph convolu…