6 accepted papers
Parameter Sharing (PS) is widely used to improve efficiency in Multi-Agent Reinforcement Learning (MARL), but it can limit behavioral diversity and degrade performance. This limitation stems from gradient conflicts among agents on shared weights, which hinders effective policy learning. To fully cha…
Learning discriminative point-wise representations remains the central challenge in scene-level, correspondence-based point cloud registration. Most existing methods process two frames independently during the early stage and introduce cross-frame interaction only at coarsest stages. Such delayed in
Point Cloud Registration is a critical and challenging task in computer vision. Recent advancements have predominantly embraced a coarse-to-fine matching mechanism with the key to matching the superpoints located in patches with inter-frame consistent structures. However previous methods still face…
Monocular 3D object detection has attracted widespread attention due to its potential to accurately obtain object 3D localization from a single image at a low cost. Depth estimation is an essential but challenging subtask of monocular 3D object detection due to the ill-posedness of 2D to 3D mapping.…
Estimating keypoint scale and orientation is crucial to extracting invariant features under significant geometric changes. Recently, the estimators based on self-supervised learning have been designed to adapt to complex imaging conditions. Such learning-based estimators generally predict a single s…
Human beings acquire the ability of image classification through visual concept learning, in which the process of concept formation involves intertwined searches of common properties and concept descriptions. However, in most image classification algorithms using deep convolutional neural network (C…