Abstract
AbstractDense video captioning aims to locate multiple events in an untrimmed video and generate captions for each event. Previous methods experienced difficulties in establishing the multimodal feature relationship between frames and captions, resulting in low accuracy of the generated captions. To address this problem, a novel Dense Video Captioning Model Based on Local Attention (DVCL) is proposed. DVCL employs a 2D temporal differential CNN to extract video features, followed by feature encoding using a deformable transformer that establishes the global feature dependence of the input sequence. Then DIoU and TIoU are incorporated into the event proposal match algorithm and evaluation algorithm during training, to yield more accurate event proposals and hence increase the quality of the captions. Furthermore, an LSTM based on local attention is designed to generate captions, enabling each word in the captions to correspond to the relevant frame. Extensive experimental results demonstrate the effectiveness of DVCL. On the ActivityNet Captions dataset, DVCL performs significantly better than other baselines, with improvements of 5.6%, 8.2%, and 15.8% over the best baseline in BLEU4, METEOR, and CIDEr, respectively.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.