3 accepted papers
Multi-task reinforcement learning (MTRL) seeks to learn a unified policy for diverse tasks, but often suffers from gradient conflicts across tasks. Existing masking-based methods attempt to mitigate such conflicts by assigning task-specific parameter masks. However, our empirical study shows that co
KAN-based networks, while offering improved interpretability compared to traditional models used in medical image segmentation, often struggle with limited adaptability to diverse imaging environments, making them less ideal for such tasks. To address this issue, we propose a Fourier Kolmogorov–Arno…
Existing Salient Object Ranking (SOR) aims to infer ranking of salient objects based on their saliency degree. However, it tends to only focus on salient objects while neglecting non-salient ones. This coarse-grained ranking limits the performance of downstream tasks. For instance, in image retrieva…