# MovingFashion: a Benchmark for the Video-to-Shop Challenge

arXiv: https://arxiv.org/abs/2110.02627 · HTML: https://mlsanity.com/paper/2110.02627/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [Improved Embeddings with Easy Positive Triplet Mining](https://mlsanity.com/paper/1904.04370/) (2019)
- [Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification](https://mlsanity.com/paper/2104.14913/) (2021)
- [Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification](https://mlsanity.com/paper/1803.09882/) (2018)
- [Robust Re-Identification by Multiple Views Knowledge Distillation](https://mlsanity.com/paper/2007.04174/) (2020)
- [Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images](https://mlsanity.com/paper/1804.05287/) (2018)
- [Revisiting Temporal Modeling for Video-based Person ReID](https://mlsanity.com/paper/1805.02104/) (2018)
- [Spatially and Temporally Efficient Non-local Attention Network for Video-based Person Re-Identification](https://mlsanity.com/paper/1908.01683/) (2019)

## Built on by (most cited first, 4 of 4)

- [Multi-queue Momentum Contrast for Microvideo-Product Retrieval](https://mlsanity.com/paper/2212.11471/) (2022)
- [Cross-view Semantic Alignment for Livestreaming Product Recognition](https://mlsanity.com/paper/2308.04912/) (2023)
- [Cross-Domain Product Representation Learning for Rich-Content E-Commerce](https://mlsanity.com/paper/2308.05550/) (2023)
- [FashionLens: Toward Versatile Fashion Image Retrieval via Task-Adaptive Learning](https://mlsanity.com/paper/2605.22552/) (2026)

## Abstract (arXiv)

Retrieving clothes which are worn in social media videos (Instagram, TikTok) is the latest frontier of e-fashion, referred to as "video-to-shop" in the computer vision literature. In this paper we present MovingFashion, the first publicly available dataset to cope with this challenge. MovingFashion is composed of 14855 social videos, each one of them associated to e-commerce "shop" images where the corresponding clothing items are clearly portrayed. In addition, we present a network for retrieving the shop images in this scenario, dubbed SEAM Match-RCNN. The model is trained by image-to-video domain adaptation, allowing to use video sequences where only their association with a shop image is given, eliminating the need of millions of annotated bounding boxes. SEAM Match-RCNN builds an embedding, where an attention-based weighted sum of few frames (10) of a social video is enough to individuate the correct product within the first 5 retrieved items in a 14K+ shop element gallery with an accuracy of 80%. This provides the best performance on MovingFashion, comparing exhaustively against the related state-of-the-art approaches and alternative baselines.