DeepSeek-VL: Real-World Vision-Language Understanding Model

Konsep Inti

DeepSeek-VL aims to enhance real-world vision-language understanding through diverse data construction, efficient model architecture, and strategic training approaches.

Abstrak

DeepSeek-VL is an open-source Vision-Language (VL) Model designed for real-world applications. The model focuses on three key dimensions: Data Construction, Model Architecture, and Training Strategy. The dataset includes web screenshots, PDFs, OCR, charts, and knowledge-based content for comprehensive representation. The model incorporates a hybrid vision encoder for high-resolution image processing within a fixed token budget efficiently. A proficient Vision-Language Model should possess strong language abilities while balancing vision capabilities. DeepSeek-VL showcases superior user experiences in real-world applications with state-of-the-art performance across various benchmarks.

Statistik

DeepSeek-VL family includes 1.3B and 7B models publicly accessible.
Pretraining dataset compiled from Common Crawl, Web Code, E-books, Educational Materials.
Supervised Fine-Tuning Data sources include ShareGPT4V, LAION-GPTV, LVIS-Instruct4V.

Kutipan

"DeepSeek-VL showcases superior user experiences as a vision-language chatbot in real-world applications."
"We strive to ensure our data is diverse, scalable and extensively covers real-world scenarios."
"Our approach is structured around three key dimensions: Data Construction, Model Architecture, Training Strategy."

Wawasan Utama Disaring Dari

DeepSeek-VL

by Haoyu Lu,Wen... pada arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05525.pdf

Pertanyaan yang Lebih Dalam

How can the balance between language and multimodal capabilities be maintained effectively during training?

During training, maintaining a balance between language and multimodal capabilities is crucial for the overall performance of large multimodal models like DeepSeek-VL. One effective strategy to achieve this balance is through careful data curation and training methodologies:

Data Construction: The dataset used for pretraining should include a diverse range of visual-text data sources to enhance cross-modal understanding capabilities. By incorporating various types of data such as image-caption pairs, table/chart data, web code, OCR text recognition, and more, the model gains exposure to different modalities.

Training Strategy: Implementing a strategic approach where both vision-language adaptor and language model are trained simultaneously can help in balancing the development of both modalities. It's essential to gradually adjust the ratio of language to multimodal data during training stages while ensuring that neither modality dominates excessively.

Hybrid Vision Encoder: Utilizing a hybrid vision encoder that efficiently processes high-resolution images while maintaining detailed information extraction at lower resolutions helps in capturing critical semantic details across various visual tasks without compromising token economy.

Joint Pretraining Approach: Engaging in joint vision-language pretraining with an emphasis on integrating significant proportions of language data alongside multimodal inputs ensures that linguistic proficiency is preserved throughout the process.

By following these strategies diligently throughout the training pipeline – from initial VL adaptor warmup to joint vision-language pretraining and supervised fine-tuning – DeepSeek-VL can maintain an effective balance between its language and multimodal capabilities.

What are the potential implications of incorporating individual differences into human-automated vehicle interaction?

Incorporating individual differences into human-automated vehicle interaction has several potential implications:

Personalized User Experience: By considering individual characteristics such as driving habits, preferences, cognitive abilities, etc., automated vehicles can tailor their interactions with users based on specific needs or requirements.

Enhanced Safety Measures: Understanding how individuals interact with technology allows automated vehicles to adapt safety protocols accordingly. For example, adjusting response times or alerts based on driver behavior patterns.

Improved Accessibility: Taking into account diverse user profiles enables automated vehicles to provide accessible features for individuals with disabilities or special requirements.

Behavioral Analysis & Prediction: Incorporating individual differences facilitates better analysis and prediction of user behaviors within automated vehicles leading to proactive measures for comfort or safety enhancements.

Efficient Communication Strategies: Tailoring communication methods based on individual preferences enhances information delivery within automated vehicles resulting in clearer instructions or alerts tailored to each user’s comprehension level.

Overall, by acknowledging and integrating individual differences into human-automated vehicle interaction systems, we pave the way for enhanced personalization, safety measures customization efficiency improvements.

How does the use of extensive pretraining impact general intelligence in large multimodal models?

Extensive pretraining plays a pivotal role in shaping general intelligence within large multimodal models like DeepSeek-VL:
1Comprehensive Knowledge Acquisition: Extensive pretraining exposes models to vast amounts of diverse real-world datasets enabling them to acquire broad knowledge spanning multiple domains including text-based content (e.g., books), images (e.g., charts), web pages among others.
2Enhanced Cross-Modal Understanding: Through extensive exposure during pretraining phases encompassing both textual inputs along with visual cues/models develop robust cross-modal understanding capabilities allowing them comprehend complex scenarios effectively
3Improved Adaptability: Models undergo rigorous learning processes which enhance their adaptability towards new tasks/scenarios due comprehensive representation they have been exposed during extended periods aiding quicker adaptation when faced novel challenges
4Language Proficiency Preservation: Effective management competitive dynamics observed between different modalities ensures preservation strong linguistic skills even after prolonged multi-modality trainings preventing degradation over time
5State-of-the-Art Performance: Extensive pretrained models exhibit superior performance across wide range benchmarks owing rich representations acquired thorough intensive learning phases fostering innovations applications requiring advanced AI solutions
In conclusion extensive pretrained significantly impacts general intelligence by equipping large multilinguals deep knowledge base versatile skill sets making them adept handling varied real-world tasks efficiently