A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation

arXiv:2207.02410v2 Announce Type: replace-cross
Abstract: In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels and other noisy labels. Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions, which unfortunately is unavailable in many real tasks. Furthermore, because the objective function for disambiguation is usually elaborately designed on the whole training set, …

abstract arxiv classification cs.cv cs.lg curriculum design filter image labels multiple paper set strategy study type