An Image Grid Can Be Worth a Video: Zero-shot Video Question Answering
  Using a VLM
Wonkyun Kim Changin Choi Wonseok Lee Wonjong Rhee
Stimulated by the sophisticated reasoning capabilities of recent Large
Language Models (LLMs), a variety of strategies for bridging video modality
have been devised. A prominent strategy involves Video Language Models
(VideoLMs), which train a learnable interface with video data to connect
advanced vision encoders with LLMs. Recently, an alternative strategy has
surfaced, employing readily available foundation models, such as VideoLMs and
LLMs, across multiple stages for modality bridging. In this study, we introduce
a simple yet novel strategy where only a single Vision Language Model (VLM) is
utilized. Our starting point is the plain insight that a video comprises a
series of images, or frames, interwoven with temporal information. The essence
of video comprehension lies in adeptly managing the temporal aspects along with
the spatial details of each frame. Initially, we transform a video into a
single composite image by arranging multiple frames in a grid layout. The
resulting single image is termed as an image grid. This format, while
maintaining the appearance of a solitary image, effectively retains temporal
information within the grid structure. Therefore, the image grid approach
enables direct application of a single high-performance VLM without
necessitating any video-data training. Our extensive experimental analysis
across ten zero-shot video question answering benchmarks, including five
open-ended and five multiple-choice benchmarks, reveals that the proposed Image
Grid Vision Language Model (IG-VLM) surpasses the existing methods in nine out
of ten benchmarks.