BIMBA: Selective-Scan Compression for Long-Range Video Question
  Answering
Authors: name: Md Mohaiminul Islam, name: Tushar Nagarajan, name: Huiyu Wang, name: Gedas Bertasius, name: Lorenzo Torresani

Video Question Answering (VQA) in long videos poses the key challenge of
extracting relevant information and modeling long-range dependencies from many
redundant frames. The self-attention mechanism provides a general solution for
sequence modeling, but it has a prohibitive cost when applied to a massive
number of spatiotemporal tokens in long videos. Most prior methods rely on
compression strategies to lower the computational cost, such as reducing the
input length via sparse frame sampling or compressing the output sequence
passed to the large language model (LLM) via space-time pooling. However, these
naive approaches over-represent redundant information and often miss salient
events or fast-occurring space-time patterns. In this work, we introduce BIMBA,
an efficient state-space model to handle long-form videos. Our model leverages
the selective scan algorithm to learn to effectively select critical
information from high-dimensional video and transform it into a reduced token
sequence for efficient LLM processing. Extensive experiments demonstrate that
BIMBA achieves state-of-the-art accuracy on multiple long-form VQA benchmarks,
including PerceptionTest, NExT-QA, EgoSchema, VNBench, LongVideoBench, and
Video-MME. Code, and models are publicly available at
https://sites.google.com/view/bimba-mllm.