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Scops self supervised co-part segmentation

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SCOPS: Self-Supervised Co-Part
Segmentation
UC Merced, NVIDIA
Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Mi...

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The problem
• Fully supervised
• Costly for the annotation work
• Hard to generalize to unseen categories
• Self-supervise...

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Contributions
• Geometric concentration
• Geometric Concentration Loss
• Robustness to variations
• Equivariance Loss
• Se...

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Scops self supervised co-part segmentation

  1. 1. SCOPS: Self-Supervised Co-Part Segmentation UC Merced, NVIDIA Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Ming-Hsuan Yang, and Jan Kautz
  2. 2. The problem • Fully supervised • Costly for the annotation work • Hard to generalize to unseen categories • Self-supervised • Learn part segmentations that are semantically consistent across different object instances, given only an image collection of the same object category. • Class agnostic
  3. 3. Contributions • Geometric concentration • Geometric Concentration Loss • Robustness to variations • Equivariance Loss • Semantic consistency • Semantic Consistency Loss • Objects as union of parts • Saliency Constraint
  4. 4. Overall Framework • Backbone: DeepLab-V2(ResNet50) • Output: 𝑅 = 𝐹(𝐼; 𝜃𝑓) ∈ [0,1] 𝐾+1 ∗𝐻∗𝑊 • K is the number of parts
  5. 5. Geometric Concentration Loss • Observation: • Pixels belonging to the same object part are spatially concentrated or form a connected component. • Minimize the variance of spatial probability distribution • The part center for a part k along axis u
  6. 6. Equivariance Loss • Random spatial Transform Ts • Random appearance perturbation Ta Transformed part center
  7. 7. Semantic Consistency Loss Pretrained on ILSVRC Avoid the bias towards background

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