Valley2: Exploring Multimodal Models with Scalable Vision-Language Design (2501.05901v2)

Abstract: Recently, vision-LLMs have made remarkable progress, demonstrating outstanding capabilities in various tasks such as image captioning and video understanding. We introduce Valley2, a novel multimodal LLM designed to enhance performance across all domains and extend the boundaries of practical applications in e-commerce and short video scenarios. Notably, Valley2 achieves state-of-the-art (SOTA) performance on e-commerce benchmarks, surpassing open-source models of similar size by a large margin (79.66 vs. 72.76). Additionally, Valley2 ranks second on the OpenCompass leaderboard among models with fewer than 10B parameters, with an impressive average score of 67.4. The code and model weights are open-sourced at https://github.com/bytedance/Valley.

• The paper introduces Valley2, a novel multimodal large language model with a scalable vision-language design optimized for e-commerce and short video applications.
• Valley2 achieves state-of-the-art performance on e-commerce benchmarks (79.66 score) and ranks second on the OpenCompass leaderboard by leveraging high-quality datasets, advanced architecture including the Eagle Module, and Chain-of-Thought post-training.
• The model's innovative architecture supports scalable visual token handling, reducing distortions and improving performance with extreme aspect ratios, demonstrating a path for enhancing MLLMs without excessive computational cost.

Overview of Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

The paper "Valley2: Exploring Multimodal Models with Scalable Vision-Language Design" introduces Valley2, an advanced multimodal LLM (MLLM) designed to enhance performance in e-commerce and short video applications. This work focuses on overcoming existing limitations in open-source multimodal models by providing innovative approaches to model architecture, data utilization, and training methodologies.

Key Contributions and Methodological Innovations

Valley2 distinguishes itself through several crucial innovations that collectively enhance its performance on e-commerce benchmarks and video understanding tasks:

1. High-Quality Datasets and Benchmarks: The authors present meticulously curated datasets specifically tailored for e-commerce and short video domains. These datasets include multimodal inputs and entail extensive image-video sequences, supporting the model's advanced reasoning capabilities. Valley2 achieves state-of-the-art (SOTA) performance on e-commerce benchmarks, significantly surpassing similarly sized open-source models, evidenced by its impressive score of 79.66 compared to the 72.76 of its peers.
2. Advanced Model Architecture: The paper details several architectural enhancements, including the integration of a large visual vocabulary, convolutional adapters, and the Eagle Module. These components collectively enhance the model's ability to process and understand complex, real-world scenarios. The innovative Eagle Module, for instance, allows the system to reduce distortions and improves the handling of extreme aspect ratios, optimizing performance in ultra-long video and image contexts.
3. Chain-of-Thought (CoT) Post-Training: This approach is employed to further refine Valley2's reasoning capabilities. The enhancement of reasoning skills through CoT training suggests potential avenues for future iterations and applications of MLLMs. This step significantly elevates Valley2's performance, as evidenced by its notable ranking on the OpenCompass leaderboard.

Results and Implications

Valley2 not only achieves SOTA performance on specific benchmarks but also ranks second in the OpenCompass leaderboard among models with fewer than 10 billion parameters, with an average score of 67.4. This demonstrates its competitiveness and efficacy in handling a wide range of multimodal tasks. Moreover, the model's architecture allows for scalable visual token support, crucial for processing complex and high-resolution inputs encountered in real-world applications.

Implications and Future Directions

The paper's findings have considerable implications for both theoretical development and practical applications. The advancements in scalable vision-language architecture can inform future research in embedding multimodal capabilities into more compact and efficient models. The architectural innovations, particularly in token compression and model flexibility, provide a pathway for enhancing MLLM performance without proportionate increases in computational overhead.

Looking forward, the research suggests expanding Valley2's capabilities to encompass audio processing and multimedia problem-solving, as indicated in their outlined "Coming Soon" section. These extensions propose to integrate audio modalities and develop more comprehensive benchmarks, further bridging the gap between academic MLLM performance and practical, application-ready systems.

In summary, the Valley2 model represents a significant step forward in the development of efficient and scalable multimodal systems. Its innovations provide a robust foundation for future advancements in integrating vision, language, and potentially audio, thereby broadening the scope of applications within various industry domains, including e-commerce and beyond.