Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning

TL;DR

Ziya-Visual introduces bilingual large vision-language models that leverage multi-task instruction tuning and multi-modal training to enhance image-text understanding and generation in both English and Chinese.

Contribution

The paper presents Ziya-Visual, a novel bilingual LVLM with multi-task instruction tuning, multi-stage training, and adaptation modules, enabling effective multi-modal dialogue in English and Chinese.

Findings

Achieves competitive performance on English image-text tasks.

Demonstrates effective Chinese multi-modal understanding and generation.

Outperforms existing LVLMs in various benchmarks.

Abstract

Recent advancements enlarge the capabilities of large language models (LLMs) in zero-shot image-to-text generation and understanding by integrating multi-modal inputs. However, such success is typically limited to English scenarios due to the lack of large-scale and high-quality non-English multi-modal resources, making it extremely difficult to establish competitive counterparts in other languages. In this paper, we introduce the Ziya-Visual series, a set of bilingual large-scale vision-language models (LVLMs) designed to incorporate visual semantics into LLM for multi-modal dialogue. Composed of Ziya-Visual-Base and Ziya-Visual-Chat, our models adopt the Querying Transformer from BLIP-2, further exploring the assistance of optimization schemes such as instruction tuning, multi-stage training and low-rank adaptation module for visual-language alignment. In addition, we stimulate the understanding ability of GPT-4 in multi-modal scenarios, translating our gathered English image-text datasets into Chinese and generating instruction-response through the in-context learning method. The experiment results demonstrate that compared to the existing LVLMs, Ziya-Visual achieves competitive performance across a wide range of English-only tasks including zero-shot image-text retrieval, image captioning, and visual question answering. The evaluation leaderboard accessed by GPT-4 also indicates that our models possess satisfactory image-text understanding and generation capabilities in Chinese multi-modal scenario dialogues. Code, demo and models are available at ~\url{https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1}.