LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection

TL;DR

LD-DETR introduces a novel loop decoder transformer that enhances video moment retrieval and highlight detection by addressing semantic overlap, local feature extraction, and decoding limitations, achieving superior results on multiple benchmarks.

Contribution

The paper proposes LD-DETR, a new model that improves multimodal feature alignment, local feature extraction, and decoding efficiency for video understanding tasks.

Findings

Outperforms state-of-the-art on QVHighlight, Charades-STA, and TACoS datasets.

Effectively mitigates semantic overlap issues in datasets.

Enhances local feature extraction and decoding in multimodal video analysis.

Abstract

Video Moment Retrieval and Highlight Detection aim to find corresponding content in the video based on a text query. Existing models usually first use contrastive learning methods to align video and text features, then fuse and extract multimodal information, and finally use a Transformer Decoder to decode multimodal information. However, existing methods face several issues: (1) Overlapping semantic information between different samples in the dataset hinders the model's multimodal aligning performance; (2) Existing models are not able to efficiently extract local features of the video; (3) The Transformer Decoder used by the existing model cannot adequately decode multimodal features. To address the above issues, we proposed the LD-DETR model for Video Moment Retrieval and Highlight Detection tasks. Specifically, we first distilled the similarity matrix into the identity matrix to mitigate the impact of overlapping semantic information. Then, we designed a method that enables convolutional layers to extract multimodal local features more efficiently. Finally, we fed the output of the Transformer Decoder back into itself to adequately decode multimodal information. We evaluated LD-DETR on four public benchmarks and conducted extensive experiments to demonstrate the superiority and effectiveness of our approach. Our model outperforms the State-Of-The-Art models on QVHighlight, Charades-STA and TACoS datasets. Our code is available at https://github.com/qingchen239/ld-detr.