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Found 3 papers, 1 papers with code
Diverse and Tailored Image Generation for Zero-shot Multi-label Classification
Our approach introduces a novel image generation framework that produces multi-label synthetic images of unseen classes for classifier training.
Positive Label Is All You Need for Multi-Label Classification
Our paper addresses label noise in MLC by introducing a positive and unlabeled multi-label classification (PU-MLC) method.
Two-Stage Co-Segmentation Network Based on Discriminative Representation for Recovering Human Mesh From Videos
However, most of the existing methods focus on the temporal consistency of videos, while ignoring the spatial representation in complex scenes, thus failing to recover a reasonable and smooth human mesh sequence under extreme illumination and chaotic backgrounds. To alleviate this problem, we propose a two-stage co-segmentation network based on discriminative representation for recovering human body meshes from videos.
