6 accepted papers
This paper introduces EA6D, a novel diffusion-based framework for 6D pose estimation that operates effectively in any environment. Traditional pose estimation methods struggle with the variability and complexity of real-world scenarios, often leading to overfitting on controlled datasets and poor ge…
Generative models are widely used to produce synthetic images with annotations, alleviating the burden of image collection and annotation for training deep visual models. However, challenges such as limited image diversity, noisy pseudo labels, and domain gaps between synthetic and real images often…
Deep learning has achieved promising performance on semantic segmentation, but few works focus on semantic segmentation at the fine-grained level. Fine-grained semantic segmentation requires recognizing and distinguishing hundreds of sub-categories. Due to the high similarity of different sub-catego…
Many recent 6D pose estimation methods exploited object 3D models to generate synthetic images for training because labels come for free. However, due to the domain shift of data distributions between real images and synthetic images, the network trained only on synthetic images fails to capture rob…
Automatic pain recognition is paramount for medical diagnosis and treatment. The existing works fall into three categories: assessing facial appearance changes, exploiting physiological cues, or fusing them in a multi-modal manner. However, (1) appearance changes are easily affected by subjective fa…
The state-of-art 6D object pose detection methods use convolutional neural networks to estimate objects' 6D poses from RGB images. However, they require huge numbers of images with explicit 3D annotations such as 6D poses, 3D bounding boxes and 3D keypoints, either obtained by manual labeling or inf…