  Image search stands as a pivotal task in multimedia and computer vision,
finding applications across diverse domains, ranging from internet search to
medical diagnostics. Conventional image search systems operate by accepting
textual or visual queries, retrieving the top-relevant candidate results from
the database. However, prevalent methods often rely on single-turn procedures,
introducing potential inaccuracies and limited recall. These methods also face
the challenges, such as vocabulary mismatch and the semantic gap, constraining
their overall effectiveness. To address these issues, we propose an interactive
image retrieval system capable of refining queries based on user relevance
feedback in a multi-turn setting. This system incorporates a vision language
model (VLM) based image captioner to enhance the quality of text-based queries,
resulting in more informative queries with each iteration. Moreover, we
introduce a large language model (LLM) based denoiser to refine text-based
query expansions, mitigating inaccuracies in image descriptions generated by
captioning models. To evaluate our system, we curate a new dataset by adapting
the MSR-VTT video retrieval dataset to the image retrieval task, offering
multiple relevant ground truth images for each query. Through comprehensive
experiments, we validate the effectiveness of our proposed system against
baseline methods, achieving state-of-the-art performance with a notable 10\%
improvement in terms of recall. Our contributions encompass the development of
an innovative interactive image retrieval system, the integration of an
LLM-based denoiser, the curation of a meticulously designed evaluation dataset,
and thorough experimental validation.
