Low-resolution Visual Recognition via Deep Feature Distillation

By Mingjian Zhu, Kai Han, Chao Zhang, Jinlong Lin, Yunhe Wang. 2019

42 citations recorded by the source corpus.

Here we study the low-resolution visual recognition problem. Conventional methods are usually trained on images with large ROIs (regions of interest), while the regions and insider images are often small and blur in real-world applications. Therefore, deep neural networks learned on high-resolution images cannot be directly used for recognizing low-resolution objects. To overcome this challenging problem, we propose to use the teacher-student learning paradigm for distilling useful feature information from a pre-trained deep model on high-resolution visual data. In practice, a distillation loss is used to seek the perceptual consistency of low-resolution images and high-resolution images. By simultaneously optimizing the recognition loss and distillation loss, we formulate a novel low-resolution recognition approach. Experiments conducted on benchmarks demonstrate that the proposed method is capable to learn well-performed models for recognizing low-resolution objects, which is superior to the state-of-the-art methods.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Knowledge Distillation: A Survey (2020, methodology): “Recently, knowledge distillation has been used successfully for solving the complex image classiﬁcation problems (Zhu et al. 2019; Bagherinezhad et al. 2018; Peng et al. 2019b; Li and Hoiem 2017; Chen et al. 2018a; Wang et al. 2019b; Mukherjee et al. 2019).”