4 accepted papers
Despite recent progress in time-series foundation models, challenges persist in improving representation learning and adapting to diverse downstream tasks. We introduce a General Time-series Model (GTM), which advances representation learning via a novel frequency-domain attention mechanism that cap…
Task-oriented grasping (TOG) involves grasping specific parts of an object based on a task instruction. Existing methods generally integrate semantic analysis with grasp detection, resulting in low efficiency and poor generalization when dealing with new tasks and hardware. To address these problems
Root cause analysis is crucial for cloud-native systems. However, existing supervised approaches ignore the potential of unlabeled data, which is frequent in the cloud-native root cause analysis scenarios. Moreover, the class-imbalanced distribution of faults presents obstacles to applying semi-supe…
Recent advancements in deep neural networks have made remarkable leap-forwards in dense image prediction. However, the issue of feature alignment remains as neglected by most existing approaches for simplicity. Direct pixel addition between upsampled and local features leads to feature maps with mis…