The recent advancements in the research area demonstrate a strong focus on leveraging deep learning and parameter-efficient transfer learning (PETL) techniques to address complex, real-world problems across various domains. Key trends include the integration of multi-modal data, the development of scalable and efficient models, and the emphasis on diversity and informativeness in data selection. In the field of ecology and conservation, there is a notable shift towards multi-species identification models that can generalize across species, reducing the need for species-specific training. Similarly, in archaeology, the introduction of large-scale, open-access datasets is enabling the application of advanced deep learning techniques to uncover hidden archaeological features. The use of PETL methods is also prominent in low-resolution face recognition and few-shot learning scenarios, where these techniques offer a balance between performance and computational efficiency. Additionally, the importance of data diversity over sheer quantity is being recognized in few-shot relation classification, suggesting a paradigm shift in how datasets are curated and utilized. Overall, the field is moving towards more efficient, diverse, and multi-modal approaches that can be applied across a wide range of tasks and domains.