Adapting Dense Vision-Language Relationships for Multi-label Classification with Partial Label
Learning multi-label image classification with incomplete annotations is a challenging task that has been widely studied for its superior trade-off between high efficiency and less labor consumption on large-scale datasets. Predominant methods rely on strong prior assumptions to recover the missing semantics from partial annotations. However, these statistic priors suffer from unstable semantic mistakes and thus lead to catastrophic overfitting. Toward this end, we propose a Language-driven Dense Semantic Adaptor (LDSA) that excavates prior-adaptive relationships from multimodal pretrained CLIP models. In our approach, the densely contrastive adaptor is first proposed to construct dense v...
| Authors | Cheng Chen, Yifan Zhao, Jia Li |
|---|---|
| Primary category | cs.CV |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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