6 accepted papers
While recent Transformer and Mamba architectures have advanced point cloud representation learning, they are typically developed for single-task or single-domain settings. Directly applying them to multi-task domain generalization (DG) leads to degraded performance. Transformers effectively model gl
The lactation performance of Saanen dairy goats, renowned for their high milk yield, is intrinsically linked to their body size, making accurate 3D body measurement essential for assessing milk production potential, yet existing reconstruction methods lack goat-specific authentic 3D data. To address
With the rapid advancement of smart farming towards large-scale livestock operations, the demand for model generalization in cross-pen behavior recognition has significantly increased. Traditional deep learning models suffer from substantial performance degradation due to variations in illumination
Recent works attempt to extend Graph Convolution Networks (GCNs) to point clouds for classification and segmentation tasks. These works tend to sample and group points to create smaller point sets locally and mainly focus on extracting local features through GCNs, while ignoring the relationship bet…
In this paper, we present PCoTTA, an innovative, pioneering framework for Continual Test-Time Adaptation (CoTTA) in multi-task point cloud understanding, enhancing the model's transferability towards the continually changing target domain. We introduce a multi-task setting for PCoTTA, which is pract…
Animal behaviour can reflect the health and physiological stage of the animal. Animal behaviour recognition is a vital part of automated farming systems. Although image-based deep learning algorithms can accurately identify animal behaviour, the lack of data on animal abnormal behaviour makes the pr…