5 accepted papers
Integrating Large Language Models (LLMs) into time series tasks has yielded impressive performance. While some works aim to enhance accuracy by explicitly designing step-by-step reasoning into prompts, such explicit Chain-of-Thought (CoT) approaches are difficult to generalize to time series. This i…
Knowledge retrieval and response generation are fundamental to task-oriented dialogue systems. However, dialogue context frequently contains noisy or irrelevant information, leading to sub-optimal result in knowledge retrieval. One possible approach to retrieving knowledge is to manually annotate st…
With the rapid advancement of synthetic speech technology, the challenges posed by audio deepfakes have become increasingly severe. Despite notable progress in synthetic speech detection, existing algorithms exhibit limited generalization to unknown attacks. To address these challenges, we propose W…
Given the intricacy and variability of anatomical structures in medical images, some methods employ shape priors to constrain segmentation. However, limited by the representational capability of these priors, existing approaches often struggle to capture diverse target structure morphologies. To add…
Recent advances in deep learning significantly boost the performance of salient object detection (SOD) at the expense of labeling larger-scale per-pixel annotations. To relieve the burden of labor-intensive labeling, deep unsupervised SOD methods have been proposed to exploit noisy labels generated…