MetaSearch: Incremental Product Search via Deep Meta-Learning

By Qi Wang, Xinchen Liu, Wu Liu, Anan Liu, Wenyin Liu, Tao Mei. 2020

63 citations recorded by the source corpus.

With the advancement of image processing and computer vision technology, content-based product search is applied in a wide variety of common tasks, such as online shopping, automatic checkout systems, and intelligent logistics. Given a product image as a query, existing product search systems mainly perform the retrieval process using predefined databases with fixed product categories. However, real-world applications often require inserting new categories or updating existing products in the product database. When using existing product search methods, the image feature extraction models must be retrained and database indexes must be rebuilt to accommodate the updated data, and these operations incur high costs for data annotation and training time. To this end, we propose a few-shot incremental product search framework with meta-learning, which requires very few annotated images and has a reasonable training time. In particular, our framework contains a multipooling-based product feature extractor that learns a discriminative representation for each product, and we also design a meta-learning-based feature adapter to guarantee the robustness of the few-shot features. Furthermore, when expanding new categories in batches during a product search, we reconstruct the few-shot features by using an incremental weight combiner to accommodate the incremental search task. Through extensive experiments, we demonstrate that the proposed framework achieves excellent performance for new products while still guaranteeing the high search accuracy of the base categories after gradually expanding new product categories without forgetting.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Han-Jia Ye
2. De-chuan Zhan
3. Da-Wei Zhou
4. Prayag Tiwari
5. Xin Ning
6. Weijun Li
7. Hang Ran
8. Lusi Li

In Computer Vision and Pattern Recognition

1. Han-Jia Ye
2. De-chuan Zhan
3. Da-Wei Zhou
4. Xin Ning
5. Weijun Li
6. Prayag Tiwari
7. Hang Ran
8. Lusi Li

Citations in the corpus

• Co-Transport for Class-Incremental Learning (2021, background): “, e-commerce product search [43], video action recognition[54], natural language generation [30], multimedia retrieval [40], and social media topic mining [49].”
• A Survey on Few-Shot Class-Incremental Learning (2023, methodology): “In FSL, meta-learning enhances the learning e ff ect of the current task by utilizing data from other related tasks (Finn et al., 2017; Liu et al., 2020a; Rusu et al., 2019; Snell et al., 2017).”