What is Seed1.5-VL
Seed1.5-VL is the latest vision-language multimodal large model released by the ByteDance Seed team, featuring strong general multimodal understanding and reasoning capabilities with significantly reduced inference costs. The model consists of a 532M-parameter vision encoder and a 20B active-parameter Mixture-of-Experts (MoE) LLM. It achieved the best performance on 38 out of 60 public benchmarks, and also outperformed leading multimodal systems such as OpenAI CUA and Claude 3.7 on interactive agent tasks. The model is available to users through the Volcano Engine open API.
Main Features of Seed1.5-VL
- 2D Image Understanding: It can recognize and analyze objects, scenes, etc. in two-dimensional images, and quickly and accurately extract semantic information from image content.
- 3D Object Understanding: It supports recognition and understanding of three-dimensional objects, and can be applied in fields such as virtual reality and augmented reality to provide users with richer interactive experiences.
- Video Content Analysis: It can analyze information such as actions, emotions, and scenes in videos, providing a basis for video content recommendation, advertising placement, and more.
- Multimodal Reasoning: It combines visual and language information to perform complex reasoning tasks, such as determining the attributes of a scene or object based on images and text descriptions.
- Interactive Agent Tasks: It performs excellently in interactive agent tasks represented by GUI control and gameplay, better understanding and responding to user instructions.
Technical Principles of Seed1.5-VL
- Model Architecture: Seed1.5-VL consists of a 532M-parameter vision encoder and a 20B active-parameter Mixture-of-Experts (MoE) language model (LLM). This enables the model to fully leverage the respective advantages of visual and language information processing, and achieve multimodal understanding and reasoning through an effective fusion mechanism.
- Visual Feature Extraction: The vision encoder is based on deep learning technologies such as convolutional neural networks (CNNs) or vision Transformers (such as ViT), and extracts feature vectors from images. It can represent different regions or objects in an image and their spatial relationships.
- Language Feature Extraction: The language encoder is based on the Transformer architecture (such as BERT or GPT), processes text data into tokens, and obtains contextual embeddings of the text through the encoder.
- Multimodal Fusion: Multimodal fusion is the core part of Seed1.5-VL, used to integrate visual and language features.
- Training Process: The training process of Seed1.5-VL involves learning from large amounts of data and optimizing model parameters. Training objectives are selected based on systematic evidence and evaluation, and are generalizable across modalities. The model needs to consider design trade-offs such as generative versus discriminative training and input data representation methods.
- Adaptability and Optimization: Seed1.5-VL is adapted for specific downstream tasks, mainly through fine-tuning. The model optimizes model size and computational efficiency through model compression and quantization, making it easier to deploy on different devices.
Project Address of Seed1.5-VL
- Project Official Website：https://seed.bytedance.com/zh/tech/seed1_5_vl
- Github Repository：https://github.com/ByteDance-Seed/Seed1.5-VL
- arXiv Technical Paper：https://arxiv.org/pdf/2505.07062
Application Scenarios of Seed1.5-VL
- Image Recognition: In fields such as e-commerce and security monitoring, it performs fast and accurate recognition of objects in images.
- Video Content Analysis: In the media and entertainment industries, analyze information such as actions, emotions, and scenes in videos to provide a basis for content recommendation and advertising placement.
- Autonomous Driving: In autonomous driving systems, used to identify and interpret vehicles, pedestrians, traffic signs, etc. on the road.
- Robot Vision: Provides visual recognition and navigation functions for robots and unmanned devices.