How does layer-wise modality specialization in MoE VLMs influence inference throughput and MMBench accuracy tr

Abstract

Abstract: Real world deployments often expose modern object recognition models to domain shifts that precipitate a severe drop in accuracy. Such shifts encompass (i) variations in low level image statistics, (ii) changes in object pose and viewpoint, (iii) partial occlusion, and (iv) visual confusion across adjacent classes. To mitigate this degradation, we introduce the Re-Thinking Vision Language Model (RT-VLM) framework. The foundation of this framework is a unique synthetic dataset generation pipeline that produces images annotated with"4-Clues": precise bounding boxes, class names, detailed object-

Research Question

How does layer-wise modality specialization in MoE VLMs influence inference throughput and MMBench accuracy trade-offs under distribution shift from natural to synthetic image inputs?

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