Questioning Matters: A Controlled Study of Question Generation in Conversational Image Retrieval

Abstract

Conversational Image Retrieval (CIR) refines image search through multi-turn interaction, where the Questioner plays a central role in eliciting information about the user’s target. However, existing CIR systems are commonly evaluated in end-to-end settings, making it difficult to determine whether performance gains originate from questioning strategies, retrieval backbones, answering behavior, or interaction protocols. We introduce a controlled benchmark for studying questioning strategies in CIR. The proposed benchmark isolates the Questioner module by keeping the retriever, answerer, dialogue budget, decoding setting, and evaluation pipeline fixed across all methods. Under this unified protocol, we compare representative strategies including Blind, Text-based, Text-based Recon, Top-K Guided, and Hybrid. Experimental analysis reveals a clear stage-dependent trade-off between efficiency and discriminative capability. Text-based strategies remain computationally lightweight but become increasingly vulnerable to contextual drift over extended interaction, whereas Top-K Guided improves retrieval refinement at the cost of higher inference latency. Overall, the proposed benchmark provides a reproducible framework for analyzing conversational questioning behavior in CIR systems.