BIMBA: Selective-Scan Compression for Long-Range Video Question
  Answering
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.
67d2f46bbc86adc45acd3888
Md Mohaiminul Islam
67d2f46bbc86adc45acd3889
Tushar Nagarajan
67d2f46bbc86adc45acd388a
Huiyu Wang
67d2f46bbc86adc45acd388b
Gedas Bertasius
admin_assigned
2025-03-14T11:25:32.212Z
65d60993c2c6418adc99206a
/avatars/fb1f9d5af7022bddf4cb090cd477108d.svg
Gedas Bertasius
user
gberta
67d2f46bbc86adc45acd388c
Lorenzo Torresani
BIMBA: Selective-Scan Compression for Long-Range Video Question
  Answering
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.
BIMBA, an efficient state-space model using the selective scan algorithm, improves accuracy on long-form VQA benchmarks by selecting critical information and reducing token sequences for LLM processing.
self-attention mechanism spatiotemporal tokens selective scan algorithm state-space model long-form VQA LLM processing PerceptionTest NExT-QA EgoSchema VNBench LongVideoBench Video-MME