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- 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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| 2. |
- Robinson, Andreas, 1975-, et al.
(författare)
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Distractor-aware video object segmentation
- 2021
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Ingår i: Pattern Recognition. DAGM GCPR 2021. - Cham : Springer International Publishing. - 9783030926588 - 9783030926595 ; , s. 222-234
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Konferensbidrag (refereegranskat)abstract
- Semi-supervised video object segmentation is a challenging task that aims to segment a target throughout a video sequence given an initial mask at the first frame. Discriminative approaches have demonstrated competitive performance on this task at a sensible complexity. These approaches typically formulate the problem as a one-versus-one classification between the target and the background. However, in reality, a video sequence usually encompasses a target, background, and possibly other distracting objects. Those objects increase the risk of introducing false positives, especially if they share visual similarities with the target. Therefore, it is more effective to separate distractors from the background, and handle them independently.We propose a one-versus-many scheme to address this situation by separating distractors into their own class. This separation allows imposing special attention to challenging regions that are most likely to degrade the performance. We demonstrate the prominence of this formulation by modifying the learning-what-to-learn method to be distractor-aware. Our proposed approach sets a new state-of-the-art on the DAVIS val dataset, and improves over the baseline on the DAVIS test-dev benchmark by 4.8 percent points.
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