FashionM3: Multimodal, Multitask, and Multiround Fashion Assistant based on Unified Vision-Language Model
Fashion styling and personalized recommendations are pivotal in modern retail, contributing substantial economic value in the fashion industry. With the advent of vision-language models (VLM), new opportunities have emerged to enhance retailing through natural language and visual interactions. This work proposes FashionM3, a multimodal, multitask, and multiround fashion assistant, built upon a VLM fine-tuned for fashion-specific tasks. It helps users discover satisfying outfits by offering multiple capabilities including personalized recommendation, alternative suggestion, product image generation, and virtual try-on simulation. Fine-tuned on the novel FashionRec dataset, comprising 331,124 multimodal dialogue samples across basic, personalized, and alternative recommendation tasks, FashionM3 delivers contextually personalized suggestions with iterative refinement through multiround interactions. Quantitative and qualitative evaluations, alongside user studies, demonstrate FashionM3's superior performance in recommendation effectiveness and practical value as a fashion assistant.
Code (0)
등록된 구현이 없습니다.
Tasks
Image GenerationLanguage ModelingLanguage ModellingVirtual Try-onSimilar Papers 제목 키워드 기반
FashionMV: Product-Level Composed Image Retrieval with Multi-View Fashion Data
Composed Image Retrieval (CIR) retrieves target images using a reference image paired with modification text. Despite rapid advances, all existing methods and datasets operate at the image level -- a single reference ima…
Image RetrievalChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
Charts play a vital role in data visualization, understanding data patterns, and informed decision-making. However, their unique combination of graphical elements (e.g., bars, lines) and textual components (e.g., labels,…
Data VisualizationDecision MakingInstruction FollowingLanguage Modeling+1Deep tensor networks with matrix product operators
We introduce deep tensor networks, which are exponentially wide neural networks based on the tensor network representation of the weight matrices. We evaluate the proposed method on the image classification (MNIST, Fashi…
Classificationimage-classificationImage ClassificationTensor NetworksBatch-Ensemble Stochastic Neural Networks for Out-of-Distribution Detection
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications. In this paper we propose…
BIG-bench Machine LearningOut-of-Distribution DetectionOut of Distribution (OOD) DetectionUncertainty QuantificationPILOT: Policy-Informed Learned Optimization for Adaptive Deep Network Training
Despite the central role of optimization in deep learning, most optimizers rely on update structures whose functional form is fixed before training begins. This static design can limit their ability to respond to changin…