In this paper we propose novel Deformable Part Networks (DPNs) to learn pose-invariant representations for 2D object recognition. In contrast to the state-of-the-art pose-aware networks such as CapsNet sabour2017dynamic and STN jaderberg2015spatial , DPNs can be naturally interpreted as an efficient solver for a challenging detection problem, namely Localized Deformable Part Models (LDPMs) where localization is introduced to DPMs as another latent variable for searching for the best poses of objects over all pixels and (predefined) scales. In particular we construct DPNs as sequences of such LDPM units to model the semantic and spatial relations among the deformable parts as hierarchical composition and spatial parsing trees. Empirically our 17-layer DPN can outperform both CapsNets and STNs significantly on affNIST sabour2017dynamic , for instance, by 19.19 and 12.75 , respectively, with better generalization and better tolerance to affine transformations.
In terms of functionality, some pose-aware network operations or modules as plug-in are proposed for existing networks. For instance, dilated convolution @cite_14 supports exponential expansion of the receptive field ( window) without loss of resolution or coverage and thus can help networks capture multi-scale information. Deformable Convolutional Networks (DCNs) @cite_20 proposed a more flexible convolutional operator that introduces pixel-level deformation, estimated by another network, into 2D convolution. Spatial Transformer Networks (STNs) @cite_34 learn pose-invariant representations by sequential applications of a localization network, a parameterized grid generator and a sampler.
@cite_34 @cite_14 @cite_20
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Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations. State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy. Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. The code would be released.