  Bilinear pooling (BLP) refers to a family of operations recently developed
for fusing features from different modalities predominantly developed for VQA
models. A bilinear (outer-product) expansion is thought to encourage models to
learn interactions between two feature spaces and has experimentally
outperformed `simpler' vector operations (concatenation and
element-wise-addition/multiplication) on VQA benchmarks. Successive BLP
techniques have yielded higher performance with lower computational expense and
are often implemented alongside attention mechanisms. However, despite
significant progress in VQA, BLP methods have not been widely applied to more
recently explored video question answering (video-QA) tasks. In this paper, we
begin to bridge this research gap by applying BLP techniques to various
video-QA benchmarks, namely: TVQA, TGIF-QA, Ego-VQA and MSVD-QA. We share our
results on the TVQA baseline model, and the recently proposed
heterogeneous-memory-enchanced multimodal attention (HME) model. Our
experiments include both simply replacing feature concatenation in the existing
models with BLP, and a modified version of the TVQA baseline to accommodate BLP
we name the `dual-stream' model. We find that our relatively simple integration
of BLP does not increase, and mostly harms, performance on these video-QA
benchmarks. Using recently proposed theoretical multimodal fusion taxonomies,
we offer insight into why BLP-driven performance gain for video-QA benchmarks
may be more difficult to achieve than in earlier VQA models. We suggest a few
additional `best-practices' to consider when applying BLP to video-QA. We
stress that video-QA models should carefully consider where the complex
representational potential from BLP is actually needed to avoid computational
expense on `redundant' fusion.
