SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model
Multimodal embedding models aim to yield informative unified representations
that empower diverse cross-modal tasks. Despite promising developments in the
evolution from CLIP-based dual-tower architectures to large vision-language
models, prior works still face unavoidable challenges in real-world
applications and business scenarios, such as the limited modality support,
unstable training mechanisms, and industrial domain gaps. In this work, we
introduce SAIL-Embedding, an omni-modal embedding foundation model that
addresses these issues through tailored training strategies and architectural
design. In the optimization procedure, we propose a multi-stage training scheme
to boost the multifaceted effectiveness of representation learning.
Specifically, the content-aware progressive training aims to enhance the
model's adaptability to diverse downstream tasks and master enriched
cross-modal proficiency. The collaboration-aware recommendation enhancement
training further adapts multimodal representations for recommendation scenarios
by distilling knowledge from sequence-to-item and ID-to-item embeddings while
mining user historical interests. Concurrently, we develop the stochastic
specialization and dataset-driven pattern matching to strengthen model training
flexibility and generalizability. Experimental results show that SAIL-Embedding
achieves SOTA performance compared to other methods in different retrieval
tasks. In online experiments across various real-world scenarios integrated
with our model, we observe a significant increase in Lifetime (LT), which is a
crucial indicator for the recommendation experience. For instance, the model
delivers the 7-day LT gain of +0.158% and the 14-day LT gain of +0.144% in the
Douyin-Selected scenario. For the Douyin feed rank model, the match features
produced by SAIL-Embedding yield a +0.08% AUC gain.
68ef0081486b78128f0e33ba
Lin Lin
68ef0081486b78128f0e33bb
Jiefeng Long
68ef0081486b78128f0e33bc
Zhihe Wan
68ef0081486b78128f0e33bd
Yuchi Wang
68ef0081486b78128f0e33be
Dingkang Yang
68ef0081486b78128f0e33bf
Shuang Yang
68ef0081486b78128f0e33c0
Yueyang Yao
68ef0081486b78128f0e33c1
Xu Chen
68ef0081486b78128f0e33c2
Zirui Guo
68ef0081486b78128f0e33c3
Shengqiang Li
68ef0081486b78128f0e33c4
Weiran Li
68ef0081486b78128f0e33c5
Hanyu Li
68ef0081486b78128f0e33c6
Yaling Mou
68ef0081486b78128f0e33c7
Yan Qiu
68ef0081486b78128f0e33c8
Haiyang Yu
68ef0081486b78128f0e33c9
Xiao Liang
68ef0081486b78128f0e33ca
Hongsheng Li
68ef0081486b78128f0e33cb
Chao Feng
SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model
Multimodal embedding models aim to yield informative unified representations
that empower diverse cross-modal tasks. Despite promising developments in the
evolution from CLIP-based dual-tower architectures to large vision-language
models, prior works still face unavoidable challenges in real-world
applications and business scenarios, such as the limited modality support,
unstable training mechanisms, and industrial domain gaps. In this work, we
introduce SAIL-Embedding, an omni-modal embedding foundation model that
addresses these issues through tailored training strategies and architectural
design. In the optimization procedure, we propose a multi-stage training scheme
to boost the multifaceted effectiveness of representation learning.
Specifically, the content-aware progressive training aims to enhance the
model's adaptability to diverse downstream tasks and master enriched
cross-modal proficiency. The collaboration-aware recommendation enhancement
training further adapts multimodal representations for recommendation scenarios
by distilling knowledge from sequence-to-item and ID-to-item embeddings while
mining user historical interests. Concurrently, we develop the stochastic
specialization and dataset-driven pattern matching to strengthen model training
flexibility and generalizability. Experimental results show that SAIL-Embedding
achieves SOTA performance compared to other methods in different retrieval
tasks. In online experiments across various real-world scenarios integrated
with our model, we observe a significant increase in Lifetime (LT), which is a
crucial indicator for the recommendation experience. For instance, the model
delivers the 7-day LT gain of +0.158% and the 14-day LT gain of +0.144% in the
Douyin-Selected scenario. For the Douyin feed rank model, the match features
produced by SAIL-Embedding yield a +0.08% AUC gain.