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MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training

NeurIPS 2022 · Video › Video segmentation and tracking · approach: Mask propagation for unsupervised video segmentation · 28 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
train a query-based image segmentation model and match query embeddings across framesMinVIS with Swin-L · OVISAP: 28.9 -> 39.4Helped
train a query-based image segmentation model without video-based trainingMinVIS with Swin-L · YouTube-VIS 2021AP: 52.6 -> 55.3Helped
train with only 1% of labeled frames (ablation)MinVIS with Swin-L · YouTube-VIS 2021AP: 55.3 -> 52.9Hurt
train with only 1% of labeled frames (ablation)MinVIS with Swin-L · OVISAP: 39.4 -> 31.7Hurt
replace query matching with mask-overlap heuristics (ablation)MinVIS · YouTube-VIS 2019AP: 61.6 -> 58.2Hurt
replace query matching with mask-overlap heuristics (ablation)MinVIS · OVISAP: 39.4 -> 31.7Hurt
combine mask-overlap heuristics with query matching (ablation)MinVIS · YouTube-VIS 2019AP: 61.6 -> 61.3Hurt
combine mask-overlap heuristics with query matching (ablation)MinVIS · OVISAP: 39.4 -> 39.1No clear effect
add supervised matching using the original frame sampling (ablation)MinVIS · YouTube-VIS 2019AP: 61.6 -> 61.0Hurt
add supervised matching using the original frame sampling (ablation)MinVIS · YouTube-VIS 2021AP: 55.3 -> 54.4Hurt
add supervised matching using the original frame sampling (ablation)MinVIS · OVISAP: 39.4 -> 38.7Hurt
add supervised matching with consecutive-frame sampling (ablation)MinVIS · OVISAP: 39.4 -> 39.6Helped

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