论文标题
Bowtie Networks：联合识别和新颖的合成的生成建模
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis
论文作者
论文摘要
我们提出了一项新的任务，即几次识别和新颖的视图综合：只给出一个或几个新的对象的图像，从任意视图中只有类别注释，我们的目标是同时学习对象分类器并从新视图点生成该类型的对象的图像。尽管现有工作主要通过多任务为共享功能表示形式学习两个或多个任务，但我们采用不同的观点。我们专注于生成模型与判别模型之间的相互作用与合作，以促进知识以互补方向跨任务流动的方式。为此，我们提出了与反馈循环共同学习3D几何和语义表示的Bowtie网络。对具有挑战性的细粒识别数据集的实验评估表明，我们的合成图像从多种观点是现实的，并显着提高了作为数据增强方式的识别性能，尤其是在低数据策略中。代码和预训练模型在https://github.com/zpbao/bowtie_networks上发布。
We propose a novel task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints. While existing work copes with two or more tasks mainly by multi-task learning of shareable feature representations, we take a different perspective. We focus on the interaction and cooperation between a generative model and a discriminative model, in a way that facilitates knowledge to flow across tasks in complementary directions. To this end, we propose bowtie networks that jointly learn 3D geometric and semantic representations with a feedback loop. Experimental evaluation on challenging fine-grained recognition datasets demonstrates that our synthesized images are realistic from multiple viewpoints and significantly improve recognition performance as ways of data augmentation, especially in the low-data regime. Code and pre-trained models are released at https://github.com/zpbao/bowtie_networks.
