Does the predictive expert caching strategy in ExpertFlow reduce object existence hallucination (POPE accuracy

Abstract

Abstract: Large vision language models (LVLMs) often suffer from object hallucination, producing objects not present in the given images. While current benchmarks for object hallucination primarily concentrate on the presence of a single object class rather than individual entities, this work systematically investigates multi-object hallucination, examining how models misperceive (e.g., invent nonexistent objects or become distracted) when tasked with focusing on multiple objects simultaneously. We introduce Recognition-based Object Probing Evaluation (ROPE), an automated evaluation protocol that consid

Research Question

Does the predictive expert caching strategy in ExpertFlow reduce object existence hallucination (POPE accuracy) more effectively for rare vs. frequent object classes in multimodal MoE-VLMs, and how does this trade-off scale with model size and number of cached experts?

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