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• HeBA processes visual tokens via 2D depthwise-separable convolutions to preserve spatial correlations, while processing text tokens via dense linear projections to capture semantic relationships.method 95%
• HeBA employs a compression bottleneck (D -> D/4) that forces the model to learn compact, robust features and acts as a structural regularizer.method 95%
• HeBA uses Kaiming initialization strategy instead of zero-initialization to ensure sufficient initial gradient flow and accelerate convergence.method 90%
• HeBA achieves new state-of-the-art performance on 11 few-shot benchmarks.result 90%
• HeBA's architecturally specialized design achieves superior stability and accuracy compared to conventional approaches.result 85%
• The method assumes access to a pre-trained VLM like CLIP, which might not be available or practical for all use cases.limitation 90%
• HeBA can be compute-intensive for certain configurations and its performance may vary based on the specific downstream application.limitation 85%
• HeBA has the potential to replace existing parameter-efficient fine-tuning methods that are either too parameter-heavy or inflexible.market 75%

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