The field of visual recognition and detection is witnessing significant advancements with the introduction of novel architectures and techniques. Researchers are exploring new ways to improve the accuracy and efficiency of models, particularly in areas such as few-shot object detection, remote sensing change detection, and online take and release detection. The integration of attention mechanisms, state space models, and transformer-based architectures is leading to state-of-the-art performance in various tasks, including image classification, semantic segmentation, and object detection. Notably, the Mamba architecture is being extensively used and modified to address specific challenges in these areas. Some noteworthy papers in this regard include A2Mamba, which proposes a powerful Transformer-Mamba hybrid network architecture, and Iwin Transformer, which introduces a novel position-embedding-free hierarchical vision transformer. AtrousMamba is another notable work that effectively balances the extraction of fine-grained local details with the integration of global contextual information. These innovative approaches are pushing the boundaries of what is possible in visual recognition and detection, and are likely to have a significant impact on the field in the coming years.