AI RESEARCH
HeBA: Heterogeneous Bottleneck Adapters for Robust Vision-Language Models
arXiv CS.CV
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ArXi:2603.16653v1 Announce Type: new Adapting large-scale Vision-Language Models (VLMs) like CLIP to downstream tasks often suffers from a "one-size-fits-all" architectural approach, where visual and textual tokens are processed uniformly by wide, generic adapters. We argue that this homogeneity ignores the distinct structural nature of the modalities -- spatial locality in images versus semantic density in text. To address this, we propose HeBA (Heterogeneous Bottleneck Adapter), a unified architectural framework that.