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Found 4 papers, 1 papers with code
Learning Canonical 3D Object Representation for Fine-Grained Recognition
By incorporating 3D shape and appearance jointly in a deep representation, our method learns the discriminative representation of the object and achieves competitive performance on fine-grained image recognition and vehicle re-identification.
Prototype-Guided Saliency Feature Learning for Person Search
Existing person search methods integrate person detection and re-identification (re-ID) module into a unified system.
Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation
Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the two domains in a global or category-aware manner.
Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation
To overcome this limitation, we introduce a learnable module, cylindrical convolutional networks (CCNs), that exploit cylindrical representation of a convolutional kernel defined in the 3D space.
