Few-Shot Learning for Monocular Depth Estimation Based on Local Object Relationship

Li, Shuai; Shi, Jiaying; Song, Wenfeng; Hao, Aimin; Qin, Hong

doi:10.1109/ictai.2019.00-97

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…instance segmentation [267], [267], [268], texture segmentation [269], [270], medical/biological image segmentation [271], [272], [273], [274] object detection general objects [275], [276], [277], [278], air vehicles [279], RGB-D objects [280] other applications image generation [37], [48], [69], [252], [281], [282], [283], [284], [285], image retrieval [111], [286], gaze estimation [287], depth estimation [288], localization [289], scene graph prediction [290], image-based person re-identification [291], [292], image colorization [293], color constancy [294] Video video classification general video classification [176], [233], [295], [296], gesture recognition [297], [298], action recognition [119], [299], [300], [301], …”

Section: Applicationsmentioning

confidence: 99%

Learning from Very Few Samples: A Survey

Lu¹,

Gong²,

Ye³

et al. 2020

Preprint

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Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning technologies, little surveys or reviews for FSL are available until now. In this context, we extensively review 300+ papers of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history as well as the current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review the latest advances on these topics. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.

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