  To build scalable models for challenging real-world tasks, it is important to
learn from diverse, multi-modal data in various forms (e.g., videos, text, and
images). Among the existing works, a plethora of them have focused on
leveraging large but cumbersome cross-modal architectures. Regardless of their
effectiveness, larger architectures unavoidably prevent the models from being
extended to real-world applications, so building a lightweight VL architecture
and an efficient learning schema is of great practical value. In this paper, we
propose an Efficient Video-Language Model (dubbed as E-ViLM) and a masked video
modeling (MVM) schema, assisted with a semantic vector-quantized tokenizer. In
particular, our E-ViLM learns to reconstruct the semantic labels of masked
video regions, produced by the pre-trained vector-quantized tokenizer, which
discretizes the continuous visual signals into labels. We show that with our
simple MVM task and regular VL pre-training modelings, our E-ViLM, despite its
compactness, is able to learn expressive representations from Video-Language
corpus and generalize well to extensive Video-Language tasks including video
question answering, text-to-video retrieval, etc. In particular, our E-ViLM
obtains obvious efficiency improvements by reaching competing performances with
faster inference speed, i.e., our model reaches $39.3$% Top-$1$ accuracy on the
MSRVTT benchmark, retaining $91.4$% of the accuracy of state-of-the-art larger
VL architecture with only $15%$ parameters and $94.8%$ fewer GFLOPs. We also
provide extensive ablative studies that validate the effectiveness of our
proposed learning schema for E-ViLM.

Computer Vision and Pattern Recognition