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DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level.

, , , and . CVPR Workshops, page 33-38. Computer Vision Foundation / IEEE, (2019)

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Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-World Training Data?, , , , , , and . CoRR, (2017)Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions., , , , and . CoRR, (2019)ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments., , , , and . CoRR, (2020)Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments., , , and . CoRR, (2020)Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition., , , , , and . Sensors, 15 (5): 10923-10947 (2015)Weather Classification by Utilizing Synthetic Data., , , , and . Sensors, 22 (9): 3193 (2022)An Efficient and Scalable Collection of Fly-Inspired Voting Units for Visual Place Recognition in Changing Environments., , , , and . IEEE Robotics Autom. Lett., 7 (2): 2527-2534 (2022)OpenSceneVLAD: Appearance Invariant, Open Set Scene Classification., , , , and . ICRA, page 4578-4584. IEEE, (2022)Patch-DrosoNet: Classifying Image Partitions With Fly-Inspired Models For Lightweight Visual Place Recognition., , , , and . CoRR, (2023)A-MuSIC: An Adaptive Ensemble System For Visual Place Recognition In Changing Environments., , , , and . CoRR, (2023)