Fashion complementary image generation (CIG) aims to create garments that stylistically match a seed item based on user intent, making it a natural multimodal grounding problem where models must interpret language in visual context. Existing CIG benchmarks rely on rigid template prompts (e.g.,"a photo of a skirt"), failing to reflect natural user queries and obscuring model behavior across levels of linguistic specificity. We introduce fashion complementary image generation with free-form instructions, a multimodal language-grounding setting where a model generates a compatible garment from a seed image and a natural-language instruction. To this end, we enrich three CIG benchmarks with low-, medium-, and high-specificity instructions generated by a vision-language model and validated by human annotators. We instantiate the task with StyleFlow, a Rectified Flow Matching model that jointly conditions on the seed image and instruction within a single multimodal transformer. Across image quality metrics, catalog-alignment analysis, ablations, and human evaluation, StyleFlow consistently produces instruction-aligned and stylistically coherent garments while reducing architectural complexity and inference cost relative to auxiliary-module approaches.
Matteo Attimonelli, Claudio Pomo, A. D. Bellis et al.· 0 citations
Graph models that manipulate the frequency spectrum of user-item interactions to separate preference signals from noise often report significant improvements, but concerns about evaluation rigor and reproducibility persist. We conduct a reproducibility and replicability study that examines three major families: (i) spectral denoising methods, (ii) graph signal processing (GSP) models, and (iii) spectral propagation approaches. Reproducing published pipelines reveals a polarized landscape: while several works are fully reproducible, others rely on flawed metric implementations and incomplete hyperparameter disclosures. In particular, we observe systematic inflation of Recall in the spectral denoising methods due to an implementation error, and theoretically invalid ranking metrics in GSP models due to unordered prediction lists; conversely, the graph filtering models are consistently reproducible. Beyond reproduction, we establish a unified evaluation protocol on four datasets with consistent splits and hyperparameter optimization for all baselines, showing that strong classical methods (e.g., SLIM, Item-kNN) remain highly competitive and that no single spectral model dominates across domains. We further analyze robustness under varying data sparsity and assess beyond-accuracy properties, finding that spectral filtering often improves catalog exploration even when accuracy gains are marginal. Our code is available at https://github.com/sisinflab/Mind_the_Metric_SIGIR-26.
Domenico de Gioia, Claudio Pomo, Ludovico Boratto et al.· Annual International ACM SIG...· 0 citations
