Seed 1.5-VL - A large-scale visual language multimodal model launched by ByteDance's Seed platform.

What is Seed1.5-VL?

Seed 1.5-VL is the latest visual-language multimodal model released by ByteDance's Seed team. It boasts powerful general-purpose multimodal understanding and reasoning capabilities, with significantly reduced inference costs. The model consists of a 532M-parameter visual encoder and a 20B active-parameter hybrid expert (MoE) LLM. It achieves state-of-the-art performance on 38 out of 60 publicly available benchmarks and outperforms leading multimodal systems such as OpenAI CUA and Claude 3.7 in interactive agent tasks. The model is available to users through the Volcano Engine open API.

Main functions of Seed1.5-VL

• 2D image understandingIt can identify and analyze objects and scenes in two-dimensional images, and quickly and accurately extract semantic information from the image content.
• 3D object understandingIt supports the recognition and understanding of 3D objects and can be applied to fields such as virtual reality and augmented reality, providing users with a richer interactive experience.
• Video content analysisIt can analyze information such as actions, emotions, and scenes in videos, providing a basis for video content recommendation and advertising.
• Multimodal reasoningIt combines visual and linguistic information to perform complex reasoning tasks, such as determining the attributes of a scene or object based on images and text descriptions.
• Interactive Agent TaskIt excels in interactive agent tasks, such as GUI control and gameplay, and better understands and responds to user commands.

Technical Principles of Seed1.5-VL

• Model ArchitectureSeed1.5-VL consists of a 532M-parameter visual encoder and a 20B-active-parameter hybrid expert (MoE) language model (LLM). This allows the model to fully leverage the strengths of both visual and linguistic information, achieving multimodal understanding and reasoning through an effective fusion mechanism.
• Visual feature extractionVisual encoders, based on deep learning techniques such as convolutional neural networks (CNNs) or visual transformers (such as ViT), extract feature vectors from images. They can represent different regions or objects in an image and their spatial relationships.
• Language feature extractionThe language encoder is based on the Transformer architecture (such as BERT or GPT), which processes text data into tokens and obtains the context embedding of the text through the encoder.
• Multimodal fusionMultimodal fusion is a core component of Seed1.5-VL, used to integrate visual and linguistic features.
• Training processThe training process of Seed1.5-VL involves learning from a large amount of data and optimizing model parameters. The training objective is selected based on systematic evidence and evaluation, and has cross-modal universality. The model needs to consider design trade-offs such as generative versus discriminative training and input data representation.
• Adaptability and OptimizationSeed1.5-VL makes adaptive adjustments for specific downstream tasks, mainly through fine-tuning. The model optimizes model size and computational efficiency through model compression and quantization, making it easy to deploy on different devices.

Seed1.5-VL project address

• 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 recognitionIn fields such as e-commerce and security monitoring, it enables rapid and accurate identification of objects in images.
• Video content analysisIn the media and entertainment industry, analyzing information such as actions, emotions, and scenes in videos provides a basis for content recommendation and advertising placement.
• autonomous drivingIn autonomous driving systems, it is used to identify and analyze vehicles, pedestrians, traffic signs, etc. on the road.
• Robot VisionProvides visual recognition and navigation capabilities for robots and unmanned equipment.