WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report
cs.CV, cs.CL, cs.IR
Submitted: 2026-08-25
Updated: 2026-08-25
Code: https://github.com/Tencent/WeMM-Embedding
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- GPT-4 Technical Report
- OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search
- mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data
- Douyin Multimodal Embedding Model Technical Report
- Microsoft COCO Captions: Data Collection and Evaluation Server
- How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites
- Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
- OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment
- MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
- MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models
- RzenEmbed: Towards Comprehensive Multimodal Retrieval
- VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks
- The Kinetics Human Action Video Dataset
- Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking
- MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs
- ViDoRe Benchmark V2: Raising the Bar for Visual Retrieval
- VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents
- Representation Learning with Contrastive Predictive Coding
- Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini
- EVA-CLIP: Improved Training Techniques for CLIP at Scale
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