arXiv Computation and Language
5d ago

When Better Teachers Don't Make Better Students: Revisiting Knowledge Distillation for CLIP Models in VQA

arXiv:2511.17886v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have achieved remarkable success across multimodal tasks, yet their substantial computational demands hinder ef...

By Pume Tuchinda, Parinthapat Pengpun, Romrawin Chumpu, Patomporn Payoungkhamdee, Sarana Nutanong, Peerat Limkonchotiwat
arXiv Computer Vision
4d ago

Adapting Dense Vision-Language Relationships for Multi-label Classification with Partial Label

The paper introduces Language-driven Dense Semantic Adaptor (LDSA) for multi-label image classification with incomplete annotations. LDSA leverages multimodal pretrained CLIP models to extract prior-adaptive relationships, employing a densely contrastive adaptor for visual contrastive constraints and a language-driven interactive decoder with class-specific prompt tuning. Experiments show LDSA achieves state‑of‑the‑art performance on public benchmarks and reveals implicit semantic relationships through its learning scheme.

By Cheng Chen, Yifan Zhao, Jia Li