Unifying Specialized Visual Encoders for Video Language Models
Authors: name: Jihoon Chung, name: Tyler Zhu, name: Max Gonzalez Saez-Diez, name: Juan Carlos Niebles, name: Honglu Zhou, name: Olga Russakovsky

The recent advent of Large Language Models (LLMs) has ushered sophisticated
reasoning capabilities into the realm of video through Video Large Language
Models (VideoLLMs). However, VideoLLMs currently rely on a single vision
encoder for all of their visual processing, which limits the amount and type of
visual information that can be conveyed to the LLM. Our method, MERV,
Multi-Encoder Representation of Videos, instead leverages multiple frozen
visual encoders to create a unified representation of a video, providing the
VideoLLM with a comprehensive set of specialized visual knowledge.
Spatio-temporally aligning the features from each encoder allows us to tackle a
wider range of open-ended and multiple-choice video understanding questions and
outperform prior state-of-the-art works. MERV is up to 3.7% better in accuracy
than Video-LLaVA across the standard suite video understanding benchmarks,
while also having a better Video-ChatGPT score. We also improve upon SeViLA,
the previous best on zero-shot Perception Test accuracy, by 2.2%. MERV
introduces minimal extra parameters and trains faster than equivalent
single-encoder methods while parallelizing the visual processing. Finally, we
provide qualitative evidence that MERV successfully captures domain knowledge
from each of its encoders. Our results offer promising directions in utilizing
multiple vision encoders for comprehensive video understanding.