10 accepted papers
Online mistake detection is crucial across various domains, ranging from industrial automation to educational applications, as mistakes can be corrected by the human operator after their detection due to the continuous inference on a video stream. While prior research mainly addresses procedural err…
Recently, SO(3)-equivariant methods have been explored for 3D reconstruction via Scan-to-CAD. Despite significant advancements attributed to the unique characteristics of 3D data, existing SO(3)-equivariant approaches often fall short in seamlessly integrating local and global contextual information…
Robotic assistance is a current research topic with high application value and multiple challenges. Assistive robots are used in various scenarios, such as production lines, operating tables, and elderly care. While providing effective assistance, most of the assistance tasks that current robots can…
In action anticipation, the model predicts the next future action after a certain observation period. In long-term action anticipation, this idea is further extended to predicting multiple actions and their respective duration. Thus, in this problem setting the model should not only capture relation…
In recency, neural signed distance fields have become more popular for reconstructing 3D indoor environments. While great improvements have been made due to missing incident radiance and materials in the surface estimation, current methods cannot reconstruct high-quality surfaces. To address this is…
Human action recognition is crucial for intelligent robots, especially in the realm of human-robot collaboration research. Recent advancements in human pose estimation algorithms have shifted the focus of action recognition towards skeleton-based models, which exhibit robustness to changes in backgr…
Human reasoning comprises the ability to understand and reason about the current action solely based on past information. To provide effective assistance in an eldercare or household environment an assistive robot or intelligent assistive system has to assess human actions correctly. Based on this p…
Temporal action segmentation is an essential task for understandingcomplex human activity sequences and identifying long-term dependencies between human actions. This is essential for effective non-verbal human-robot collaboration and robotic assistance to understand the underlying human intentions.
In recent years, Action Detection has become an active research topic in various fields such as human-robot interaction and assistive robots. Most of the previous methods in this field focus on temporally processing the action representation, without considering the dependencies among the action cla…
Segmenting activities in untrimmed videos remains a critical challenge to fully understand complex human activity sequences. A correct representation of temporal action relations is key for improving incorrect segmentations. We propose a self-attention-based model that refines initial segmentations…