Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation

By Hang Gao, Xizhou Zhu, Steve Lin, Jifeng Dai. 2019

73 citations recorded by the source corpus.

Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity. To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data towards a common arrangement such that semantic recognition suffers less from deformation. This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field. Yet adapting the receptive field does not quite reach the actual goal -- what really matters to the network is the "effective" receptive field (ERF), which reflects how much each pixel contributes. It is thus natural to design other approaches to adapt the ERF directly during runtime. In this work, we instantiate one possible solution as Deformable Kernels (DKs), a family of novel and generic convolutional operators for handling object deformations by directly adapting the ERF while leaving the receptive field untouched. At the heart of our method is the ability to resample the original kernel space towards recovering the deformation of objects. This approach is justified with theoretical insights that the ERF is strictly determined by data sampling locations and kernel values. We implement DKs as generic drop-in replacements of rigid kernels and conduct a series of empirical studies whose results conform with our theories. Over several tasks and standard base models, our approach compares favorably against prior works that adapt during runtime. In addition, further experiments suggest a working mechanism orthogonal and complementary to previous works.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Model Architectures

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Citations in the corpus

Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation (2020, neutral): “Another related method is deformable convolution [22,94,26],”
K-Net: Towards Unified Image Segmentation (2021, background): “Previous works [16, 18, 25, 26, 71] explore different kinds of dynamic kernels to improve the flexibility and performance of models.”
Dynamic Neural Networks: A Survey (2021, methodology): “Deformable Kernels [112] yðpÞ 1⁄4PKk1⁄41 Wðpk þ DpkÞxðpþ pkÞ Conv kernel No 7442 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.”
Dynamic Region-Aware Convolution (2020, methodology): “To prove the effectiveness of DRConv, we compare DRConv with state-of-the-art methods including [9, 36].”