The emergence of ACMix fundamentally integrates convolution and self-attention mechanisms, fully leveraging their advantages. However, it faces challenges in associating temporal sequences and struggles to achieve accurate feature sampling. Additionally, its global correlation ability makes it susceptible to interference from irrelevant information. To address these issues, we propose the Spatio-Temporal Deformable Mix Feature Extractor (STD-ME) based on ACMix. In STD-ME, we designed deformable modules for both convolution and attention branche...
