Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual
  Prompt Instruction Tuning
Authors: name: Wujian Peng, name: Lingchen Meng, name: Yitong Chen, name: Yiweng Xie, name: Yang Liu, name: Tao Gui, name: Hang Xu, name: Xipeng Qiu, name: Zuxuan Wu, name: Yu-Gang Jiang

Large Multimodal Models (LMMs) have made significant breakthroughs with the
advancement of instruction tuning. However, while existing models can
understand images and videos at a holistic level, they still struggle with
instance-level understanding that requires a more nuanced comprehension and
alignment. Instance-level understanding is crucial, as it focuses on the
specific elements that we are most interested in. Excitingly, existing works
find that the state-of-the-art LMMs exhibit strong instance understanding
capabilities when provided with explicit visual cues. Motivated by this, we
introduce an automated annotation pipeline assisted by GPT-4o to extract
instance-level information from images and videos through explicit visual
prompting for instance guidance. Building upon this pipeline, we proposed
Inst-IT, a solution to enhance LMMs in Instance understanding via explicit
visual prompt Instruction Tuning. Inst-IT consists of a benchmark to diagnose
multimodal instance-level understanding, a large-scale instruction-tuning
dataset, and a continuous instruction-tuning training paradigm to effectively
enhance spatial-temporal instance understanding capabilities of existing LMMs.
Experimental results show that, with the boost of Inst-IT, our models not only
achieve outstanding performance on Inst-IT Bench but also demonstrate
significant improvements across various generic image and video understanding
benchmarks. This highlights that our dataset not only boosts instance-level
understanding but also strengthens the overall capabilities of generic image
and video comprehension.