The field of cross-modal retrieval and object detection is moving towards more robust and accurate methods, with a focus on handling noisy labels and imperfect annotations. Researchers are exploring innovative approaches to address these challenges, including multi-level adaptive correction and alignment, graph-based spatial anomaly detection, and radar-camera fusion. These advancements have the potential to significantly improve the performance of object detection systems in various applications, including autonomous driving. Noteworthy papers include MCA, which proposes a robust 2D-3D cross-modal retrieval framework, and SpaRC-AD, which introduces a query-based end-to-end camera-radar fusion framework for planning-oriented autonomous driving. Additionally, the paper on label error detection and correction highlights the urgent need for further research in this area.