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Research map / Detection and segmentation / CNN features

Detangling People: Individuating Multiple Close People and Their Body Parts via Region Assembly

CVPR 2017 · Detection and segmentation › CNN features · approach: Context for hard detection conditions · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use global region assembly instead of connected-component groupingPerson instance segmentation · UCIinstance mean IoU (F): 41.62 -> 63.02Helped
Use global region assembly instead of greedy sequential groupingPerson instance segmentation · UCIinstance mean IoU (F): 46.88 -> 63.02Helped
Use region assembly rather than the strongest reported baselinePerson instance segmentation · UCIinstance mean IoU (F): 61.81 -> 63.02Helped
Use region assembly rather than the strongest reported baselinePerson instance segmentation · MPIIinstance mean IoU (F): 56.04 -> 57.48Helped
Use region assembly rather than CNN pose detectionPerson instance segmentation · MPIIinstance mean IoU (B): 45.48 -> 57.15Helped
Use region assembly rather than the strongest reported baselineBody part segmentation · UCIpart mean IoU (F): 37.98 -> 38.39Helped
Use region assembly rather than greedy groupingBody part segmentation · UCIpart mean IoU (B): 32.30 -> 38.56Helped
Use region assembly rather than NBest pose masksBody part segmentation · MPIIpart mean IoU (F): 28.71 -> 35.48Helped
Use region assembly rather than NBest pose masksBody part segmentation · MPIIpart mean IoU (B): 29.16 -> 35.47Helped
Apply region assembly without training on BuffyPerson individuation · Buffy episode 4, 5, 6forward score: 62.4% -> 68.22%Helped
Replace direct whole-person detection with assembling candidate body-part regions into instancesMulti-person instance and part segmentation · UCI and MPIIinstance mean IoUHelped

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