CRET: Cross-Modal Retrieval Transformer for Efficient Text-Video Retrieval

Abstract

Given a text query, the text-to-video retrieval task aims to find the relevant videos in the database. Recently, model-based (MDB) methods have demonstrated superior accuracy than embedding-based (EDB) methods due to their excellent capacity of modeling local video/text correspondences, especially when equipped with large-scale pre-training schemes like ClipBERT. Generally speaking, MDB methods take a text-video pair as input and harness deep models to predict the mutual similarity, while EDB methods first utilize modality-specific encoders to extract embeddings for text and video, then evaluate the distance based on the extracted embeddings. Notably, MDB methods cannot produce explicit representations for text and video, instead, they have to exhaustively pair the query with every database item to predict their mutual similarities in the inference stage, which results in significant inefficiency in practical applications. In this work, we propose a novel EDB method CRET (Cross-modal REtrieval Transformer), which not only demonstrates promising efficiency in retrieval tasks, but also achieves better accuracy than existing MDB methods. The credits are mainly attributed to our proposed Cross-modal Correspondence Modeling (CCM) module and Gaussian Estimation of Embedding Space (GEES) loss. Specifically, the CCM module is composed by transformer decoders and a set of decoder centers. With the help of the learned decoder centers, the text/video embeddings can be efficiently aligned, without suffering from pairwise model-based inference. Moreover, to balance the information loss and computational overhead when sampling frames from a given video, we present a novel GEES loss, which implicitly conducts dense sampling in the video embedding space, without suffering from heavy computational cost. Extensive experiments show that without pre-training on extra datasets, our proposed CRET outperforms the state-of-the-art MDB methods that were pre-trained on additional datasets, meanwhile still shows promising efficiency in retrieval tasks.