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- Brissman, Emil, et al.
(författare)
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Predicting Signed Distance Functions for Visual Instance Segmentation
- 2021
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Ingår i: 33rd Annual Workshop of the Swedish-Artificial-Intelligence-Society (SAIS). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665442367 - 9781665442374 ; , s. 5-10
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Konferensbidrag (refereegranskat)abstract
- Visual instance segmentation is a challenging problem and becomes even more difficult if objects of interest varies unconstrained in shape. Some objects are well described by a rectangle, however, this is hardly always the case. Consider for instance long, slender objects such as ropes. Anchor-based approaches classify predefined bounding boxes as either negative or positive and thus provide a limited set of shapes that can be handled. Defining anchor-boxes that fit well to all possible shapes leads to an infeasible number of prior boxes. We explore a different approach and propose to train a neural network to compute distance maps along different directions. The network is trained at each pixel to predict the distance to the closest object contour in a given direction. By pooling the distance maps we obtain an approximation to the signed distance function (SDF). The SDF may then be thresholded in order to obtain a foreground-background segmentation. We compare this segmentation to foreground segmentations obtained from the state-of-the-art instance segmentation method YOLACT. On the COCO dataset, our segmentation yields a higher performance in terms of foreground intersection over union (IoU). However, while the distance maps contain information on the individual instances, it is not straightforward to map them to the full instance segmentation. We still believe that this idea is a promising research direction for instance segmentation, as it better captures the different shapes found in the real world.
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| 2. |
- Johnander, Joakim, 1993-, et al.
(författare)
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Video Instance Segmentation with Recurrent Graph Neural Networks
- 2021
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Ingår i: Pattern Recognition. - Cham : Springer. - 9783030926588 - 9783030926595 ; , s. 206-221
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Konferensbidrag (refereegranskat)abstract
- Video instance segmentation is one of the core problems in computer vision. Formulating a purely learning-based method, which models the generic track management required to solve the video instance segmentation task, is a highly challenging problem. In this work, we propose a novel learning framework where the entire video instance segmentation problem is modeled jointly. To this end, we design a graph neural network that in each frame jointly processes all detections and a memory of previously seen tracks. Past information is considered and processed via a recurrent connection. We demonstrate the effectiveness of the proposed approach in comprehensive experiments. Our approach, operating at over 25 FPS, outperforms previous video real-time methods. We further conduct detailed ablative experiments that validate the different aspects of our approach.
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