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Lingsheng Kong

2 accepted papers

2019

Feature-Level Frankenstein: Eliminating Variations for Discriminative Recognition

CVPR 2019poster

Recent successes of deep learning-based recognition rely on maintaining the content related to the main-task label. However, how to explicitly dispel the noisy signals for better generalization remains an open issue. We systematically summarize the detrimental factors as task-relevant/irrelevant sem…

Cited by 48PDFcodeScholar
2019

Permutation-Invariant Feature Restructuring for Correlation-Aware Image Set-Based Recognition

ICCV 2019poster

We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images. We use feature restructuring to exploit the correlations of both inner&inter-set images. Specifically, the residual self-attention can effectively restructure the fe…

Cited by 38PDFScholar