  In this work, we propose an efficient Video-Language Alignment (ViLA)
network. Our ViLA model addresses both efficient frame sampling and effective
cross-modal alignment in a unified way. In our ViLA network, we design a new
learnable text-guided Frame-Prompter together with a new cross-modal
distillation (QFormer-Distiller) module. Pre-trained large image-language
models have shown promising results on problems such as visual question
answering (VQA). However, how to efficiently and effectively sample video
frames when adapting pre-trained large image-language model to video-language
alignment is still the major challenge. Compared with prior work, our ViLA
model demonstrates the capability of selecting key frames with critical
contents, thus improving the video-language alignment accuracy while reducing
the inference latency +3.3% on NExT-QA Temporal with 3.0X speed up). Overall,
our ViLA network outperforms the state-of-the-art methods on the video
question-answering benchmarks: +4.6% on STAR Interaction, +2.2% on STAR average
with 3.0X speed up, ours 2-frames out-perform SeViLA 4-frames on the VLEP
dataset with 4.2X speed-up.
