Enhancing Text-Video Retrieval Performance with Low-Salient but Discriminative Objects.

Abstract

Text-video retrieval aims to establish a matching relationship between a video and its corresponding text. However, previous works have primarily focused on salient video subjects, such as humans or animals, often overlooking Low-Salient but Discriminative Objects (LSDOs) that play a critical role in understanding content. To address this limitation, we propose a novel model that enhances retrieval performance by emphasizing these overlooked elements across video and text modalities. In the video modality, our model first incorporates a feature selection module to gather video-level LSDO features, and applies cross-modal attention to assign frame-specific weights based on relevance, yielding frame-level LSDO features. In the text modality, text-level LSDO features are captured by generating multiple object prototypes in a sparse aggregation manner. Extensive experiments on benchmark datasets, including MSR-VTT, MSVD, LSMDC, and DiDeMo, demonstrate that our model achieves state-of-the-art results across various evaluation metrics.