  This paper is on long-term video understanding where the goal is to recognise
human actions over long temporal windows (up to minutes long). In prior work,
long temporal context is captured by constructing a long-term memory bank
consisting of past and future video features which are then integrated into
standard (short-term) video recognition backbones through the use of attention
mechanisms. Two well-known problems related to this approach are the quadratic
complexity of the attention operation and the fact that the whole feature bank
must be stored in memory for inference. To address both issues, we propose an
alternative to attention-based schemes which is based on a low-rank
approximation of the memory obtained using Singular Value Decomposition. Our
scheme has two advantages: (a) it reduces complexity by more than an order of
magnitude, and (b) it is amenable to an efficient implementation for the
calculation of the memory bases in an incremental fashion which does not
require the storage of the whole feature bank in memory. The proposed scheme
matches or surpasses the accuracy achieved by attention-based mechanisms while
being memory-efficient. Through extensive experiments, we demonstrate that our
framework generalises to different architectures and tasks, outperforming the
state-of-the-art in three datasets.
