Keywords
vision-in-the-loop
active visual acquisition
long-horizon search
simulation
Abstract
The cited literature on vision-in-the-loop search ranges from Efficient Search in a Panoramic Image Database for Long-term Visual Localization to DeepVoyager-VL: Incentivizing vision-in-the-loop search for long-horizon multimodal agents and Multimodal Analysis of Image Search Intent, bringing together methods that are often evaluated under incompatible assumptions. A reference-grounded synthesis is developed through visual localization, agent memory, and multimodal retrieval. The comparison distinguishes algorithmic contribution from the evidence used to support reliability, transferability, or practical use. This perspective clarifies which conclusions travel across contexts and which remain tied to particular data or procedures. Future studies can build on the map through preregistered comparisons, sensitivity analysis, and openly documented evaluation choices.
References
Zhang, H., Zhou, J., Zhao, R., Shan, Y., Chen, J., Zhou, B., Li, B., Wang, F., Wu, J., Tao, Z., Mei, L., Yu, X., Liu, L., Chen, C., & Zhang, W. (2026). DeepVoyager-VL: Incentivizing vision-in-the-loop search for long-horizon multimodal agents. arXiv. https://doi.org/10.48550/arXiv.2608.01827
Bibi, R., Mehmood, Z., Yousaf, R.-M., Saba, T., Sardaraz, M., & Rehman, A. (2020). Query-by-visual-search: multimodal framework for content-based image retrieval. Journal of Ambient Intelligence and Humanized Computing, 11(11), 5629-5648. https://doi.org/10.1007/s12652-020-01923-1
Tang, Z., Long, Z., & Fu, X. (2023). Universal Multimodal Neural Machine Translation Via Image Retrieval from Search Engines. . https://doi.org/10.2139/ssrn.4566495
Soleymani, M., Riegler, M., & Halvorsen, P. (2017). Multimodal Analysis of Image Search Intent. Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, 251-259. https://doi.org/10.1145/3078971.3078995
Halstead, M.-A., Denman, S., Sridharan, S., Tian, Y., & Fookes, C. (2019). Multimodal clothing recognition for semantic search in unconstrained surveillance imagery. Journal of Visual Communication and Image Representation, 58, 439-452. https://doi.org/10.1016/j.jvcir.2018.12.001
Strong, G., Hoeber, O., & Gong, M. (2010). Visual Image Browsing and Exploration (Vibe): User Evaluations of Image Search Tasks. Lecture Notes in Computer Science, 424-435. https://doi.org/10.1007/978-3-642-15470-6_44
He, R., Long, S., Sun, W., & Liu, H. (2024). A Multimodal Image Registration Method for UAV Visual Navigation Based on Feature Fusion and Transformers. Drones, 8(11), 651. https://doi.org/10.3390/drones8110651
Orhan, S., & Bastanlar, Y. (2021). Efficient Search in a Panoramic Image Database for Long-term Visual Localization. 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 1727-1734. https://doi.org/10.1109/iccvw54120.2021.00198
Hamroun, M., Lajmi, S., Nicolas, H., & Amous, I. (2018). ISE: Interactive Image Search using Visual Content. Proceedings of the 20th International Conference on Enterprise Information Systems, 253-261. https://doi.org/10.5220/0006806702530261
Motter, B.-C., & Simoni, D.-A. (2007). The roles of cortical image separation and size in active visual search performance. Journal of Vision, 7(2), 6. https://doi.org/10.1167/7.2.6