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Title
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In defense of LSTMs for addressing multiple instance learning problems
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Author
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Abstract
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| LSTMs have a proven track record in analyzing sequential data. But what about unordered instance bags, as found under a Multiple Instance Learning (MIL) setting? While not often used for this, we show LSTMs excell under this setting too. In addition, we show that LSTMs are capable of indirectly capturing instance-level information using only bag-level annotations. Thus, they can be used to learn instance-level models in a weakly supervised manner. Our empirical evaluation on both simplified (MNIST) and realistic (Lookbook and Histopathology) datasets shows that LSTMs are competitive with or even surpass state-of-the-art methods specially designed for handling specific MIL problems. Moreover, we show that their performance on instance-level prediction is close to that of fully-supervised methods. |
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Language
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English
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Source (book)
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Computer Vision : ACCV 2020 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part VI
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Source (series)
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Lecture Notes in Computer Science ; 12627
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Publication
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Cham
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Springer
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2021
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ISBN
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978-3-030-69543-9
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978-3-030-69543-9
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DOI
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10.1007/978-3-030-69544-6_27
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Volume/pages
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p. 444-460
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Full text (Publisher's DOI)
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