Quizzard@INOVA Challenge 2025 -- Track A: Plug-and-Play Technique in Interleaved Multi-Image Model
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
This paper addresses two main objectives. Firstly, we demonstrate the
impressive performance of the LLaVA-NeXT-interleave on 22 datasets across three
different tasks: Multi-Image Reasoning, Documents and Knowledge-Based
Understanding and Interactive Multi-Modal Communication. Secondly, we add the
Dense Channel Integration (DCI) connector to the LLaVA-NeXT-Interleave and
compare its performance against the standard model. We find that the standard
model achieves the highest overall accuracy, excelling in vision-heavy tasks
like VISION, NLVR2, and Fashion200K. Meanwhile, the DCI-enhanced version shows
particular strength on datasets requiring deeper semantic coherence or
structured change understanding such as MIT-States_PropertyCoherence and
SlideVQA. Our results highlight the potential of combining powerful foundation
models with plug-and-play techniques for Interleave tasks. The code is
available at https://github.com/dinhvietcuong1996/icme25-inova.
