6 accepted papers
Multi-source domain adaptation (MSDA) borrows intrinsic knowledge from well-annotated source domains to identify target visual signals. The main challenges are effectively mitigating cross-domain shift and extracting discriminative target features via the suitable source semantics. To overcome them,…
Source-free domain adaptation (SFDA) aims to transfer knowledge from the well-trained source model and optimize it to adapt target data distribution. SFDA methods are suitable for medical image segmentation task due to its data-privacy protection and achieve promising performances. However, cross-do…
Deep neural networks have been successfully applied in many computer vision tasks. However, these models suffer catastrophic forgetting when learning new knowledge incrementally. To overcome the stability-plasticity dilemma, class incremental learning (CIL) has been widely discussed recently. The st…
Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target…
Zero-shot learning for visual recognition has received much interest in the most recent years. However, the semantic gap across visual features and their underlying semantics is still the biggest obstacle in zero-shot learning. To fight off this hurdle, we propose an effective Low-rank Embedded Sema…
We present a multi-stream bi-directional recurrent neural network for fine-grained action detection. Recently, two-stream convolutional neural networks (CNNs) trained on stacked optical flow and image frames have been successful for action recognition in videos. Our system uses a tracking algorithm…