Unifying Specialist Image Embedding Into Universal Image Embedding

Abstract

Deep image embedding provides a way to measure the semantic similarity of two images. It plays a central role in many applications such as image search, face verification, and zero-shot learning. It is desirable to have a universal deep embedding model applicable to various domains of images. However, existing methods mainly rely on training specialist embedding models each of which is applicable to images from a single domain. In this paper, we study an important but unexplored task: how to train a single universal image embedding model to match the performance of several specialists on each specialist's domain. Simply fusing the training data from multiple domains cannot solve this problem because some domains become overfitted sooner when trained together using existing methods. Therefore, we propose to distill the knowledge in multiple specialists into a universal embedding to solve this problem. In contrast to existing embedding distillation methods that distill the absolute dista

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyfeng2020unifying

Related papers

• Learning A Unified Embedding For Visual Search At Pinterest (2019)10.85
• Efficient And Discriminative Image Feature Extraction For Universal Image Retrieval (2024)4.94
• Unifying Deep Local And Global Features For Image Search (2020)28.10
• Fuse And Attend: Generalized Embedding Learning For Art And Sketches (2022)0.00
• Imagebind: One Embedding Space To Bind Them All (2023)21.38
• Universal Model For Multi-domain Medical Image Retrieval (2020)0.00
• Image-to-image Translation For Cross-domain Disentanglement (2018)0.00
• Universal Vision-language Dense Retrieval: Learning A Unified Representation Space For Multi-modal Retrieval (2022)3.45