Forecasting Future Instance Segmentation with Learned Optical Flow and Warping
Andrea Ciamarra,Federico Becattini,Lorenzo Seidenari,Alberto Del Bimbo +3 more
- 15 Nov 2022
Vol. abs/2211.08049, pp 349-361
TL;DR: In this paper , the authors investigate the use of optical flow for predicting future semantic segmentation and propose a model that forecasts flow fields autoregressively. And such predictions are then used to guide the inference of a learned warping function that moves instance segmentations on to future frames.
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Abstract: For an autonomous vehicle it is essential to observe the ongoing dynamics of a scene and consequently predict imminent future scenarios to ensure safety to itself and others. This can be done using different sensors and modalities. In this paper we investigate the usage of optical flow for predicting future semantic segmentations. To do so we propose a model that forecasts flow fields autoregressively. Such predictions are then used to guide the inference of a learned warping function that moves instance segmentations on to future frames. Results on the Cityscapes dataset demonstrate the effectiveness of optical-flow methods.
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Citations
MemFlow: Optical Flow Estimation and Prediction with Memory
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Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time
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Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
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TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Feature Pyramid Networks for Object Detection
Tsung-Yi Lin,Piotr Dollár,Ross Girshick,Kaiming He,Bharath Hariharan,Serge Belongie +5 more
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Mask R-CNN
Kaiming He,Georgia Gkioxari,Piotr Dollár,Ross Girshick +3 more
- 20 Mar 2017
TL;DR: This work presents a conceptually simple, flexible, and general framework for object instance segmentation, which extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.
•Proceedings Article
Mask R-CNN
Kaiming He,Georgia Gkioxari,Piotr Dollár,Ross Girshick +3 more
- 20 Mar 2017
TL;DR: This work presents a conceptually simple, flexible, and general framework for object instance segmentation that outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.
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