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Mining for meaning: from vision to language through multiple networks consensus., , , and . BMVC, page 275. BMVA Press, (2018)Features in Concert: Discriminative Feature Selection meets Unsupervised Clustering., , and . CoRR, (2014)Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation., and . CoRR, (2015)UFO Depth: Unsupervised learning with flow-based odometry optimization for metric depth estimation., , , , , , and . ICRA, page 6526-6532. IEEE, (2022)Multiple Random Masking Autoencoder Ensembles for Robust Multimodal Semi-supervised Learning., and . CoRR, (2024)Self-supervised Hypergraphs for Learning Multiple World Interpretations., , , , , , , and . ICCV (Workshops), page 983-992. IEEE, (2023)Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks., , and . NeurIPS, page 7111-7125. (2021)Unsupervised Learning of Object Features from Video Sequences., and . CVPR (1), page 1142-1149. IEEE Computer Society, (2005)Discovering Texture Regularity as a Higher-Order Correspondence Problem., , , and . ECCV (2), volume 3952 of Lecture Notes in Computer Science, page 522-535. Springer, (2006)A Spectral Technique for Correspondence Problems Using Pairwise Constraints., and . ICCV, page 1482-1489. IEEE Computer Society, (2005)