Keywords
vision-in-the-loop
active visual acquisition
long-horizon search
simulation
Abstract
The reference set connects DeepVoyager-VL: Incentivizing vision-in-the-loop search for long-horizon multimodal agents with studies of Multimodal Analysis of Image Search Intent and A Multimodal Image Registration Method for UAV Visual Navigation Based on…, offering several competing ways to frame vision-in-the-loop search. The analysis follows observation timing, visual acquisition, and interactive exploration, tracing points of convergence as well as differences in terminology, measurement, and experimental design. This makes hidden assumptions visible without introducing unsupported performance claims. The contribution is a structured reading of the existing evidence, not a new synthetic benchmark claim. It provides criteria for selecting methods, interpreting metrics, and designing follow-up studies that can be independently checked.
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