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Technical Report for Soccernet 2023 -- Dense Video Captioning

arXiv 2411.00882 ↗First posted 31 Oct 2024cs.CVcited by 0

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Abstract (arXiv)

In the task of dense video captioning of Soccernet dataset, we propose to generate a video caption of each soccer action and locate the timestamp of the caption. Firstly, we apply Blip as our video caption framework to generate video captions. Then we locate the timestamp by using (1) multi-size sliding windows (2) temporal proposal generation and (3) proposal classification.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 7 references that are in the map. First posted on arXiv 31 Oct 2024.

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6 of 7 references.

References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  4. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  5. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  6. 2021VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understandingcited by 602

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