6 accepted papers
Learning to manipulate objects efficiently, particularly those involving sustained contact (e.g., pushing, sliding) and articulated parts (e.g., drawers, doors), presents significant challenges. Traditional methods, such as robot-centric reinforce-ment learning (RL), imitation learning, and hybrid t
Deep Unfolding Networks (DUNs), with their outstanding performance and partial interpretability, have revitalized the field of pan-sharpening. However, the current DUNs for pan-sharpening rely entirely on implicit deep priors, ignoring the intrinsic physical prior knowledge of multispectral image (M…
Deep Unfolding Networks (DUNs) have emerged as a powerful framework for pansharpening due to their interpretable fusion strategies. However, existing DUNs are limited by their serial iterative architectures, which hinder cross-stage and cross-modal feature interactions at different abstraction level…
Semi-supervised Learning (SSL) reduces the need for extensive annotations in deep learning, but the more realistic challenge of imbalanced data distribution in SSL remains largely unexplored. In Class Imbalanced Semi-supervised Learning (CISSL), the bias introduced by unreliable pseudo-labels can be…
Despite the progress of Semi-supervised Learning (SSL), existing methods fail to utilize unlabeled data effectively and efficiently. Many pseudo-label-based methods select unlabeled examples based on inaccurate confidence scores from the classifier. Most prior work also uses all available unlabeled…
In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo-targets undermine the training of an accurate detector. It injects noise into the student's training, leading to severe overfitting pro…