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ARXIV:2603.21886 · INTERACTIVE TEXT-TO-IMAGE RETRIEVAL · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.21886INTERACTIVE TEXT-TO-IMAGE RETRIEVALSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEZhuocheng Zhang · Xingwu Zhang · Kangheng Liang · Guanxuan Li · Richard Mccreadie · Zijun Long · arXiv
A lightweight fusion model that significantly improves interactive text-to-image retrieval by adaptively combining multi-modal feedback, outperforming existing methods with minimal parameter increase.
Opportunity summary
Pain A lightweight fusion model that significantly improves interactive text-to-image retrieval by adaptively combining multi-modal feedback, outperforming existing methods with minimal parameter increase.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A lightweight fusion model that significantly improves interactive text-to-image retrieval by adaptively combining multi-modal feedback, outperforming existing methods with minimal parameter increase. However, existing frameworks fuse multi-modal views of user feedback by simple embedding…
Recent advances in interactive text-to-image retrieval (I-TIR) use diffusion models to bridge the modality gap between the textual information need and the images to be searched, resulting in increased effectiveness. However, existing frameworks fuse…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this work, we show that this static and undifferentiated fusion indiscriminately incorporates generative noise produced by the diffusion model, leading to performance degradation…
Interactive Text-to-Image Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A lightweight fusion model that significantly improves interactive text-to-image retrieval by adaptively combining multi-modal feedback, outperforming existing methods with minimal parameter increase.
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10.48550/arXiv.2603.21886A lightweight fusion model that significantly improves interactive text-to-image retrieval by adaptively combining multi-modal feedback, outperforming existing methods with minimal parameter increase.
Abstract
Recent advances in interactive text-to-image retrieval (I-TIR) use diffusion models to bridge the modality gap between the textual information need and the images to be searched, resulting in increased effectiveness. However, existing frameworks fuse multi-modal views of user feedback by simple embedding addition. In this work, we show that this static and undifferentiated fusion indiscriminately incorporates generative noise produced by the diffusion model, leading to performance degradation for up to 55.62% samples. We further propose ADaFuSE (Adaptive Diffusion-Text Fusion with Semantic-aware Experts), a lightweight fusion model designed to align and calibrate multi-modal views for diffusion-augmented I-TIR, which can be plugged into existing frameworks without modifying the backbone encoder. Specifically, we introduce a dual-branch fusion mechanism that employs an adaptive gating branch to dynamically balance modality reliability, alongside a semantic-aware mixture-of-experts branch to capture fine-grained cross-modal nuances. Via thorough evaluation over four standard I-TIR benchmarks, ADaFuSE achieves state-of-the-art performance, surpassing DAR by up to 3.49% in Hits@10 with only a 5.29% parameter increase, while exhibiting stronger robustness to noisy and longer interactive queries. These results show that generative augmentation coupled with principled fusion provides a simple, generalizable alternative to fine-tuning for interactive retrieval.
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PROBLEM
A lightweight fusion model that significantly improves interactive text-to-image retrieval by adaptively combining multi-modal feedback, outperforming existing methods with minimal parameter increase. However, existing frameworks fuse multi-modal views of user feedback by simple...
METHOD
Recent advances in interactive text-to-image retrieval (I-TIR) use diffusion models to bridge the modality gap between the textual information need and the images to be searched, resulting in increased effectiveness. However, existing frameworks fuse multi-modal views of user fe...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this work, we show that this static and undifferentiated fusion indiscriminately incorporates generative noise produced by the diffusion model, leading to performance degradation for up to 55.62% sampl...
WHY NOW
Interactive Text-to-Image Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A lightweight fusion model that significantly improves interactive text-to-image retrieval by adaptively combining multi-modal feedback, outperforming existing methods with minimal parameter increase. However, existing frameworks fuse multi-modal views of user feedback by simple embedding addition.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Recent advances in interactive text-to-image retrieval (I-TIR) use diffusion models to bridge the modality gap between the textual information need and the images to be searched, resulting in increased effectiveness. However, existing frameworks fuse multi-modal views of user feedback by simple embedding addition.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this work, we show that this static and undifferentiated fusion indiscriminately incorporates generative noise produced by the diffusion model, leading to performance degradation for up to 55.62% samples. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Interactive Text-to-Image Retrieval moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A lightweight fusion model that significantly improves interactive text-to-image retrieval by adaptively combining multi-modal feedback, outperforming existing methods with minimal parameter increase.
Segment
Interactive Text-to-Image Retrieval
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