Attribute-Conditioned Multimodal Slot Factorization for Controllable Fashion Retrieval
This paper introduces MM-slotgate, a multimodal slot encoder that factorizes Fashion-CLIP text and image embeddings into four named attribute slots, enabling controllable fashion retrieval. On H&M, it reaches 0.7566 macro ConstraintSatisfied@10, surpassing equal-weight multimodal fusion and fCLIP text-only retrieval, with interpretable gates and substantial gains on color.
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[Submitted on 12 Aug 2026]
Title:Attribute-Conditioned Multimodal Slot Factorization for Controllable Fashion Retrieval
View a PDF of the paper titled Attribute-Conditioned Multimodal Slot Factorization for Controllable Fashion Retrieval, by Najmeh Forouzandehmehr and 3 other authors
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Abstract:Fashion retrieval often requires satisfying multiple attributes at once, such as category, color, pattern, and demographic. Monolithic embeddings mix these signals into a single vector, making attribute-specific control difficult at retrieval time. Many existing semantic-ID methods provide discrete item codes, but these codes are typically optimized as item-level or residual addresses and do not expose named, independently controllable attribute slots.
We introduce MM-slotgate, a multimodal slot encoder that factorizes Fashion-CLIP text and image embeddings into four named attribute slots. Each slot learns its own text-image gate, so visually grounded attributes such as color and pattern can rely more on image evidence, while taxonomy-oriented attributes such as category and demographic can remain more text-driven.
On H&M, using a combined slot-similarity and slot-logit retrieval score, MM-slotgate achieves 0.7566 macro ConstraintSatisfied@10, outperforming equal-weight multimodal fusion (0.7142) and fCLIP text-only retrieval (0.4755). The largest gain is on color, which improves from 0.321 to 0.889 (+0.568 absolute), as the learned color gate assigns 57.4% weight to image evidence. The learned gates are interpretable without modality supervision: color is image-leaning, category is text-leaning, and pattern and demographic lie near the middle.
The resulting slots also remain controllable: linear probes show no measured excess leakage beyond the label-correlation baseline, and quantized slot codes support targeted intervention, including a 15.3x lift for color. These results suggest that controllable fashion retrieval benefits from typed, attribute-conditioned multimodal slots rather than either a single global embedding or opaque item-level semantic IDs.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
Cite as: arXiv:2608.12570 [cs.CV]
(or arXiv:2608.12570v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.12570
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Najmeh Forouzandehmehr [view email] [v1] Wed, 12 Aug 2026 20:28:14 UTC (108 KB)
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