Deep learning features at scale for visual place recognition
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Cites background or methods from "Deep learning features at scale for..."
...We also evaluate the de-facto standard approach for loop-closure detection in robotics [23, 36], where robustness to changing conditions is critical for long-term autonomous navigation [17, 37, 46, 49, 66, 69]: FAB-MAP [20] is an image retrieval approach based on the Bag-ofWords (BoW) paradigm [62] that explicitly models the cooccurrence probability of different visual words....
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...They are often used for place recognition [1, 17, 41, 55, 66, 69] and loop-closure detection [20,25,48]....
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...They remain effective at scale [3,55,57,71] and can be robust to changing conditions [1,17,49,57,66,69]....
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...Datasets for place recognition [17, 46, 65, 69, 72] often provide query images captured under different conditions compared to the database images....
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...Learning-based localization has been proposed to solve both loop-closure detection [17, 45, 64, 66] and pose estimation [19, 31, 74]....
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360 citations
Cites background from "Deep learning features at scale for..."
...This task is one of the core problems in Computer Vision and has several practical applications, ranging from lung nodule malignancy classification [19] to the localization of mobile robots [20]....
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"Deep learning features at scale for..." refers background in this paper
...However, recent evidence suggests that features extracted from Convolutional Neural Networks (CNNs) trained on very large datasets significantly outperform SIFT features on a variety of vision tasks [3], such as object recognition [4], fine-grained recognition [5], scene recognition [6] and object detection [7]....
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...However, it is rapidly becoming apparent in the computer vision community that hand-crafted features are being outperformed by deep learnt features in various vision tasks [3-7], which prompts the question of whether we can learn better features automatically for place recognition....
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"Deep learning features at scale for..." refers background in this paper
...classification architecture [28], a Siamese network [29] and a triplet network [30]....
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