ByteDance Unveils Seed1.5-VL: A Breakthrough Vision-Language Model for Advanced Multimodal AI

The Rise of Vision-Language Models in AI

Vision-Language Models (VLMs) have become essential in developing AI systems that understand and interact across digital and real-world environments. By merging visual and textual data, these models have propelled advances in multimodal reasoning, image editing, GUI agents, robotics, and impacted sectors such as education and healthcare. However, challenges remain as VLMs still fall short of human abilities in complex tasks like 3D reasoning, object counting, creative visual interpretation, and interactive gameplay. The limited availability of rich, diverse multimodal datasets compared to textual data presents a significant hurdle, alongside the complexity of training and evaluating such data.

Introducing Seed1.5-VL: Architecture and Capabilities

ByteDance researchers have developed Seed1.5-VL, a compact yet powerful vision-language foundation model. It combines a 532 million-parameter vision encoder with a 20 billion-parameter Mixture-of-Experts large language model (LLM). Despite its efficiency, Seed1.5-VL excels by achieving top results on 38 out of 60 public VLM benchmarks, particularly in GUI control, video understanding, and visual reasoning tasks. The model is trained on trillions of multimodal tokens using sophisticated data synthesis and post-training techniques, including human feedback. Training innovations like hybrid parallelism and vision token redistribution further optimize its performance. These features make it well-suited for real-world interactive applications such as chatbots.

Seed1.5-VL’s Technical Design

The model architecture includes a vision encoder called Seed-ViT, an MLP adapter, and an LLM. Seed-ViT supports native-resolution image input through 2D rotary positional embeddings (RoPE) and processes images by dividing them into 14×14 patches, followed by average pooling and an MLP layer. Pretraining involves multiple objectives: masked image modeling, contrastive learning, and omni-modal alignment using diverse data types including images, texts, and video-audio-caption pairs.

For video encoding, Seed1.5-VL uses a Dynamic Frame-Resolution Sampling method that adapts frame rates and resolutions based on content complexity. This balances efficiency with detail and enables effective spatial-temporal understanding within a token budget, ensuring comprehensive representation across videos of varying lengths and complexities.

Extensive and Diverse Training Data

Pretraining included curating 3 trillion high-quality tokens from diverse domains. Image-text pairs sourced from the web were filtered using CLIP scores, size and aspect ratio checks, and deduplication to minimize noise. Domain-based sampling and duplication strategies addressed class imbalances by overrepresenting rare visual concepts.

Specialized datasets enhanced capabilities in OCR with annotated and synthetic text-rich images, charts, and tables. Object grounding and counting tasks leveraged bounding boxes, points, and auto-labeled web data. Additional tasks targeted 3D spatial understanding through depth annotations and video understanding using multi-frame captioning, question-answering, and temporal grounding for dynamic content analysis.

Evaluation and Performance Highlights

Seed-ViT, despite its smaller parameter size, matches or outperforms larger models like InternVL-C and EVA-CLIP on zero-shot image classification benchmarks such as ImageNet-A and ObjectNet, demonstrating high accuracy and robustness.

Seed1.5-VL shows strong multimodal reasoning, general visual question answering (VQA), document understanding, and grounding capabilities. It achieves state-of-the-art results in complex reasoning, counting, and chart interpretation tasks. The model’s unique "thinking" mode, which incorporates longer reasoning chains, further boosts its performance, showcasing its advanced visual understanding and generalization across tasks.

Pioneering Future Directions

Seed1.5-VL’s compact yet powerful design, including a 532M-parameter vision encoder and 20B-parameter Mixture-of-Experts language model, enables it to compete with and surpass models like OpenAI CUA and Claude 3.7 in various tasks. It excels in complex reasoning, OCR, diagram interpretation, 3D spatial understanding, video analysis, and agent-driven tasks such as GUI control and gameplay. The research outlines its architecture, data pipeline, and training methods, highlighting potential future improvements centered on enhancing tool use and visual reasoning capabilities.

For more details, check the Paper and Project Page. Follow the researchers on Twitter and join the ML SubReddit community with over 90k members.