The field of visual recognition and 3D mapping is rapidly evolving, with a focus on developing more efficient and accurate models. Recent research has explored the use of multi-scale features, capsule networks, and transformer-based architectures to improve performance in various tasks such as image classification, object detection, and 3D reconstruction. Notably, the integration of multi-scale features and attention mechanisms has shown promising results in capturing complex patterns and relationships in data. Additionally, the application of 3D mapping techniques to dynamic environments and indoor spaces has gained significant attention, with advances in drone-based scanning and human-AI collaborative annotation. Overall, the field is moving towards more robust and scalable models that can handle diverse and complex data.

Noteworthy papers include: MSPCaps, which proposes a novel capsule network architecture that integrates multi-scale feature learning and efficient capsule routing, achieving remarkable scalability and superior robustness. MSMVD, which exploits multi-scale image features to generate BEV features for multi-view pedestrian detection, improving detection performance and outperforming previous methods. E-ConvNeXt, which significantly reduces the parameter scale and network complexity of ConvNeXt while maintaining high accuracy performance, demonstrating a superior accuracy-efficiency balance.