ByteDance Introduces Seed1.5-VL: A Vision-Language Foundation Model
ByteDance has developed Seed1.5-VL, a vision-language foundation model that integrates visual and textual data to enhance multimodal understanding and reasoning. This model is designed to address the limitations of current Vision-Language Models (VLMs) in tasks requiring complex reasoning and interaction in both digital and real-world environments.
Advancements in Vision-Language Models
VLMs are pivotal in creating general-purpose AI systems that can process and interpret multimodal data. They have shown promise in various applications, including:
- Multimodal reasoning
- Image editing
- Graphical User Interface (GUI) agents
- Robotics
Despite these advancements, VLMs still struggle with tasks involving 3D reasoning, object counting, and creative visual interpretation. A significant challenge is the lack of rich and diverse multimodal datasets, which contrasts with the abundance of textual resources available for Language Models (LLMs).
Technical Specifications of Seed1.5-VL
Seed1.5-VL features a compact architecture with a 532 M-parameter vision encoder and a 20 B-parameter Mixture-of-Experts LLM. It has achieved top results on 38 out of 60 public VLM benchmarks, excelling in:
- GUI control
- Video understanding
- Visual reasoning
Trained on trillions of multimodal tokens, Seed1.5-VL utilizes advanced data synthesis and post-training techniques, including human feedback. Innovations in training methods, such as hybrid parallelism and vision token redistribution, contribute to its performance efficiency.
Architecture and Training Methods
The architecture of Seed1.5-VL includes:
- A custom vision encoder called Seed-ViT
- An MLP adapter
- An LLM
Seed-ViT processes images using 2D RoPE and divides them into 14×14 patches, followed by average pooling and an MLP. The pre-training process involves:
- Masked image modeling
- Contrastive learning
- Omni-modal alignment with images, text, and video-audio-caption pairs
Additionally, the model employs a Dynamic Frame-Resolution Sampling approach for video encoding, adapting frame rates and resolutions based on content complexity, thereby supporting effective spatial-temporal understanding.
Evaluation and Performance
Seed-ViT demonstrates competitive performance in vision-language tasks, matching or outperforming larger models like InternVL-C and EVA-CLIP in zero-shot image classification. Seed1.5-VL excels in:
- Multimodal reasoning
- General Visual Question Answering (VQA)
- Document understanding
- Grounding tasks
With capabilities in complex reasoning, counting, and chart interpretation, the model’s “thinking” mode incorporates longer reasoning chains, enhancing its performance in detailed visual understanding and task generalization.
Conclusion
In summary, Seed1.5-VL is a vision-language foundation model that combines a 532 M-parameter vision encoder with a 20 B-parameter Mixture-of-Experts language model. It achieves state-of-the-art results on 38 of 60 public benchmarks, particularly in complex reasoning, Optical Character Recognition (OCR), diagram interpretation, 3D spatial understanding, and video analysis. The model also excels in agent-driven tasks like GUI control and gameplay, surpassing notable models such as OpenAI CUA and Claude 3.7. Future directions for this research include enhancing tool-use and visual reasoning capabilities.
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