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VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation

CVPR 2022 · Video › Video segmentation and tracking · approach: Video semantic and panoptic segmentation · 28 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use grid-based memory matching with temporal feature aggregation and score reweightingVISOLO · YouTube-VIS 2019 valAP: 36.8 -> 38.6Helped
Use grid-based memory matching with temporal feature aggregation and score reweightingVISOLO · YouTube-VIS 2021 valAP: 34.2 -> 36.9Helped
Add score reweighting (ablation)VISOLO · YouTube-VIS 2019 valAP: 34.6 -> 35.6Helped
Add temporal aggregation for category features (ablation)VISOLO · YouTube-VIS 2019 valAP: 34.6 -> 36.4Helped
Add score reweighting to category temporal aggregation (ablation)VISOLO · YouTube-VIS 2019 valAP: 36.4 -> 37.7Helped
Add temporal aggregation for mask features (ablation)VISOLO with SR and category TA · YouTube-VIS 2019 valAP: 37.7 -> 38.6Helped
Store 2 rather than every 5 intermediate frames (ablation)VISOLO · YouTube-VIS 2019 valAP: 38.6 -> 36.7Hurt
Store 10 rather than every 5 intermediate frames (ablation)VISOLO · YouTube-VIS 2019 valAP: 38.6 -> 37.5Hurt
Store 20 rather than every 5 intermediate frames (ablation)VISOLO · YouTube-VIS 2019 valFPS: 40.0 -> 38.7Hurt
Use every 5 intermediate frames instead of storing only 2 frames (ablation)VISOLO · YouTube-VIS 2019 valAP: 36.7 -> 38.6Helped

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