DARC-CLIP: Dynamic Adaptive Refinement with Cross-Attention for Meme Understanding
Memes convey meaning through the interaction of visual and textual signals, often combining humor, irony, and offense in subtle ways. Detecting harmful or sensitive content in memes requires accurate modeling of these multimodal cues. Existing CLIP-based approaches rely on static fusion, which struggles to capture fine grained dependencies between modalities. We propose DARC-CLIP, a CLIP-based framework for adaptive multimodal fusion with a hierarchical refinement stack. DARC-CLIP introduces Adaptive Cross-Attention Refiners to for bidirectional information alignment and Dynamic Feature Adapters for task-sensitive signal adaptation. We evaluate DARC-CLIP on the PrideMM benchmark, which in...
| Authors | Qiyuan Jin |
|---|---|
| Primary category | cs.CL |
| Published | 25 April 2026 |
| Last updated at source | 28 April 2026 |
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