HeBA: Heterogeneous Bottleneck Adapters for Robust Vision-Language Models

TL;DR

HeBA introduces modality-specific structural biases into vision-language models, using heterogeneous processing and regularization techniques, resulting in improved stability and accuracy across multiple few-shot benchmarks.

Contribution

HeBA presents a novel architectural framework with modality-specific processing, bottleneck regularization, and active gradient initialization for enhanced VLM adaptation.

Findings

Achieves state-of-the-art results on 11 few-shot benchmarks.

Demonstrates improved stability and convergence speed.

Outperforms conventional homogeneous adapter designs.

Abstract

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 introduces modality-specific structural inductive biases. HeBA departs from conventional designs through three key architectural innovations: (1) Heterogeneity: It processes visual tokens via 2D depthwise-separable convolutions to preserve spatial correlations, while distinctively processing text tokens via dense linear projections to capture semantic relationships; (2) Bottleneck Regularization: Unlike standard expanding adapters, HeBA employs a compression bottleneck (D -> D/4) that explicitly forces the model to learn compact, robust features and acts as a structural regularizer; and (3) Active Gradient Initialization: We challenge the restrictive zero-initialization paradigm, utilizing a Kaiming initialization strategy that ensures sufficient initial gradient flow to accelerate convergence without compromising the frozen backbone's pre-trained knowledge. Extensive experiments demonstrate that HeBA's architecturally specialized design achieves superior stability and accuracy, establishing a new state-of-the-art on 11 few-shot benchmarks. Code is available at https://github.com/Jahid12012021/VLM-HeBA.