In Video Question Answering (VideoQA), answering general questions about a
video requires its visual information. Yet, video often contains redundant
information irrelevant to the VideoQA task. For example, if the task is only to
answer questions similar to "Is someone laughing in the video?", then all other
information can be discarded. This paper investigates how many bits are really
needed from the video in order to do VideoQA by introducing a novel Few-Bit
VideoQA problem, where the goal is to accomplish VideoQA with few bits of video
information (e.g., 10 bits). We propose a simple yet effective task-specific
feature compression approach to solve this problem. Specifically, we insert a
lightweight Feature Compression Module (FeatComp) into a VideoQA model which
learns to extract task-specific tiny features as little as 10 bits, which are
optimal for answering certain types of questions. We demonstrate more than
100,000-fold storage efficiency over MPEG4-encoded videos and 1,000-fold over
regular floating point features, with just 2.0-6.6% absolute loss in accuracy,
which is a surprising and novel finding. Finally, we analyze what the learned
tiny features capture and demonstrate that they have eliminated most of the
non-task-specific information, and introduce a Bit Activation Map to visualize
what information is being stored. This decreases the privacy risk of data by
providing k-anonymity and robustness to feature-inversion techniques, which can
influence the machine learning community, allowing us to store data with
privacy guarantees while still performing the task effectively.

本文介绍了如何使用轻量级的特征压缩模块（FeatComp）来实现 Few-Bit VideoQA 任务，该模块通过学习提取 10 位的任务特定特征，在保证精度的同时比 MPEG4 编码的视频节省了 10 万倍的存储空间，并降低了数据隐私风险。