Douyin Multimodal Embedding Model Technical Report

• URL: http://arxiv.org/abs/2608.02148v1
• Date: Mon, 03 Aug 2026 12:31:49 GMT
• Title: Douyin Multimodal Embedding Model Technical Report
• Abstract summary: Multimodal representation learning is a cornerstone of modern AI.<n>We present Douyin Multimodal Embedding (DME), a model trained in two stages to combine both strengths.
• Score: 77.9018834072698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal representation learning is a cornerstone of modern AI. By encoding multimodal queries and targets into vectors, it powers industrial search and recommendation and underpins modern agents. Real-world platforms with complex modalities and massive-scale content, such as Douyin, Xiaohongshu, and YouTube, demand both efficiency under billion-scale indexing and fine-grained discrimination for hard matching. Existing MLLM embedding models rarely satisfy both. Contrastive models are efficient but rely on pair-level supervision too coarse for fine-grained distinctions, while CoT-based models improve discrimination through explicit generation impractical to serve online. We present Douyin Multimodal Embedding (DME), a model trained in two stages to combine both strengths. Stage 1 performs large-scale contrastive pre-training that establishes a unified multimodal embedding space with broad modality and task coverage. Stage 2 supplements semantic sufficiency, the property that an embedding is grounded in retrieval-relevant evidence and preserves fine-grained counterpart-side semantics, via two mechanisms. Evidence-Grounded Typed Latent Reasoning organizes retrieval evidence through hidden-space latent reasoning, and Cross-Conditional Reconstruction enforces counterpart-side semantics through cross-directional autoregressive reconstruction. Both act only during training and add only marginal query-side overhead, so DME serves as efficiently as a standard contrastive encoder. On MMEB-v2, DME reaches state-of-the-art results at comparable scales for its 2B and 9B variants (74.8 and 78.4), with especially strong video and visual-document tasks. In production, DME delivers a 2.92% relative gain on Douyin's in-house offline evaluation set, is deployed across Douyin scenarios such as generative, image, and AI search, and yields a 0.1% Lifetime (LT) gain in online A/B testing on Douyin search.

Related papers

• R2LED: Equipping Retrieval and Refinement in Lifelong User Modeling with Semantic IDs for CTR Prediction [23.668401664583758]
  We propose a novel paradigm that equips retrieval and refinement in Lifelong User Modeling with SEmantic IDs (R2LED)<n>First, we introduce a Multi-route Mixed Retrieval for the retrieval stage. On the other hand, a mixed retrieval mechanism is proposed to efficiently retrieve candidates from both collaborative and semantic views.<n>For refinement, we design a Bi-level Fusion Refinement, including a target-aware cross-attention for route-level fusion and a gate mechanism for SID-level fusion.
  arXiv  Detail & Related papers  (2026-02-06T11:27:20Z)
• From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation [59.27094165576015]
  We propose a novel learning paradigm (UniMod) that transitions from sparse decision-making to dense reasoning traces.<n>By constructing structured trajectories encompassing evidence grounding, modality assessment, risk mapping, policy decision, and response generation, we reformulate monolithic decision tasks into a multi-dimensional boundary learning process.<n>We introduce specialized optimization strategies to decouple task-specific parameters and rebalance training dynamics, effectively resolving interference between diverse objectives in multi-task learning.
  arXiv  Detail & Related papers  (2026-01-28T09:29:40Z)
• VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection [24.88767599022225]
  Few-Shot Anomaly Detection (FSAD) has emerged as a critical paradigm for identifying irregularities using scarce normal references.<n>This study proposes VTFusion, a vision-text multimodal fusion framework tailored for FSAD.
  arXiv  Detail & Related papers  (2026-01-23T00:30:24Z)
• SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams [53.78257200138774]
  We propose a Self-Evolving Relevance Model approach (SERM), which comprises two complementary multi-agent modules.<n>We evaluate SERM in a large-scale industrial setting, which serves billions of user requests daily.
  arXiv  Detail & Related papers  (2026-01-14T14:31:16Z)
• Filling the Gaps: A Multitask Hybrid Multiscale Generative Framework for Missing Modality in Remote Sensing Semantic Segmentation [28.992992584085787]
  Multimodal learning has shown significant performance boost compared to ordinary unimodal models.<n>In real-world scenarios, multimodal signals are susceptible to missing because of sensor failures and adverse weather conditions.<n>We propose a novel Generative-Enhanced MultiModal learning Network (GEMMNet) to tackle these limitations.
  arXiv  Detail & Related papers  (2025-09-14T05:40:35Z)
• Progressive Semantic Residual Quantization for Multimodal-Joint Interest Modeling in Music Recommendation [6.790539226766362]
  We propose a novel multimodal recommendation framework with two stages.<n>In the first stage, our method generates modal-specific and modal-joint semantic IDs.<n>In the second stage, to model multimodal interest of users, a Multi-Codebook Cross-Attention network is designed.
  arXiv  Detail & Related papers  (2025-08-28T02:16:57Z)
• MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings [75.0617088717528]
  MoCa is a framework for transforming pre-trained VLM backbones into effective bidirectional embedding models.<n>MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2025-06-29T06:41:00Z)
• Robust Modality-incomplete Anomaly Detection: A Modality-instructive Framework with Benchmark [69.02666229531322]
  We introduce a pioneering study that investigates Modality-Incomplete Industrial Anomaly Detection (MIIAD)<n>We find that most existing MIAD methods perform poorly on the MIIAD Bench, leading to significant performance degradation.<n>We propose a novel two-stage Robust modAlity-aware fusing and Detecting framewoRk, abbreviated as RADAR.
  arXiv  Detail & Related papers  (2024-10-02T16:47:55Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis [47.29528724322795]
  Multimodal Sentiment Analysis (MSA) has attracted increasing attention recently. Despite significant progress, there are still two major challenges on the way towards robust MSA. We propose a generic and unified framework to address them, named Efficient Multimodal Transformer with Dual-Level Feature Restoration (EMT-DLFR)
  arXiv  Detail & Related papers  (2022-08-16T08:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.