IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model
Yatai Ji Shilong Zhang Jie Wu Peize Sun Weifeng Chen Xuefeng Xiao Sidi Yang Yujiu Yang Ping Luo
The rapid advancement of Large Vision-Language models (LVLMs) has demonstrated a spectrum of emergent capabilities. Nevertheless, current models only focus on the visual content of a single scenario, while their ability to associate instances across different scenes has not yet been explored, which is essential for understanding complex visual content, such as movies with multiple characters and intricate plots. Towards movie understanding, a critical initial step for LVLMs is to unleash the potential of character identities memory and recognition across multiple visual scenarios. To achieve the goal, we propose visual instruction tuning with ID reference and develop an ID-Aware Large Vision-Language Model, IDA-VLM. Furthermore, our research introduces a novel benchmark MM-ID, to examine LVLMs on instance IDs memory and recognition across four dimensions: matching, location, question-answering, and captioning. Our findings highlight the limitations of existing LVLMs in recognizing and associating instance identities with ID reference. This paper paves the way for future artificial intelligence systems to possess multi-identity visual inputs, thereby facilitating the comprehension of complex visual narratives like movies.
large vision-language model; ID recognition
We propose an ID-aware LVLM, IDA-VLM to recognize instance IDs across diverse scenes, towards understanding complex visual inputs, such as movies. We construct a new benchmark, MM-ID, to examine LVLMs on instance IDs recognition.