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"Kernelized Few-shot Object Detection with Efficient Integral Aggregati" by Shan Zhang, Lei Wang et al.

We design a Kernelized Few-shot Object Detector by leveraging kernelized matrices computed over multiple proposal regions, which yield expressive non-linear representations whose model complexity is learned on the fly. Our pipeline contains several modules. An Encoding Network encodes support and query images. Our Kernelized Autocorrelation unit forms the linear, polynomial and RBF kernelized representations from features extracted within support regions of support images. These features are then cross-correlated against features of a query image to obtain attention weights, and generate query proposal regions via an Attention Region Proposal Net. As the query proposal regions are many, each described by the linear, polynomial and RBF kernelized matrices, their formation is costly but that cost is reduced by our proposed Integral Region-of-Interest Aggregation unit. Finally, the Multi-head Relation Net combines all kernelized (second-order) representations with the first-order feature ....

Kernelized Few Shot Object Detector , Encoding Network , Kernelized Autocorrelation , Attention Region Proposal , Integral Region Of Interest Aggregation , Multi Head Relation Net , Computer Vision Theory , Machine Learning , Ecognition Detection , Representation Learning ,

"Class Similarity Weighted Knowledge Distillation for Continual Semanti" by Minh Hieu Phan, The Anh Ta et al.

Deep learning models are known to suffer from the problem of catastrophic forgetting when they incrementally learn new classes. Continual learning for semantic segmentation (CSS) is an emerging field in computer vision. We identify a problem in CSS: A model tends to be confused between old and new classes that are visually similar, which makes it forget the old ones. To address this gap, we propose REMINDER - a new CSS framework and a novel class similarity knowledge distillation (CSW-KD) method. Our CSW-KD method distills the knowledge of a previous model on old classes that are similar to the new one. This provides two main benefits: (i) selectively revising old classes that are more likely to be forgotten, and (ii) better learning new classes by relating them with the previously seen classes. Extensive experiments on Pascal-Voc 2012 and ADE20k datasets show that our approach outperforms state-of-the-art methods on standard CSS settings by up to 7.07% and 8.49%, respectively. ....

Computer Vision Theory , Deep Learning Architectures And Techniques , Fficient Learning And Inferences , Rouping And Shape Analysis , Representation Learning , Cene Analysis And Understanding , Ision Applications And Systems ,