In defense of LSTMs for addressing multiple instance learning problems
| Autor: | Tinne Tuytelaars, M José Oramas, Kaili Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Computer Science - Artificial Intelligence Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Artificial Intelligence (cs.AI) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Sequential data Artificial intelligence business Engineering sciences. Technology MNIST database 0105 earth and related environmental sciences |
| Zdroj: | Computer Vision : ACCV 2020 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part VI Computer Vision – ACCV 2020 ISBN: 9783030695439 ACCV (6) |
| Popis: | 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 thatLSTMs are capable of indirectly capturing instance-level information us-ing 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. accepted in ACCV 2020 (oral) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|