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Through an examination\nof its adaptive behavior, we observe that while the spatial support for its\nneural features conforms more closely than regular ConvNets to object\nstructure, this support may nevertheless extend well beyond the region of\ninterest, causing features to be influenced by irrelevant image content. To\naddress this problem, we present a reformulation of Deformable ConvNets that\nimproves its ability to focus on pertinent image regions, through increased\nmodeling power and stronger training. The modeling power is enhanced through a\nmore comprehensive integration of deformable convolution within the network,\nand by introducing a modulation mechanism that expands the scope of deformation\nmodeling. To effectively harness this enriched modeling capability, we guide\nnetwork training via a proposed feature mimicking scheme that helps the network\nto learn features that reflect the object focus and classification power of\nR-CNN features. With the proposed contributions, this new version of Deformable\nConvNets yields significant performance gains over the original model and\nproduces leading results on the COCO benchmark for object detection and\ninstance segmentation.","url_abs":"http://arxiv.org/abs/1811.11168v2","url_pdf":"http://arxiv.org/pdf/1811.11168v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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