Summary of Evc-mf: End-to-end Video Captioning Network with Multi-scale Features, by Tian-zi Niu et al.
EVC-MF: End-to-end Video Captioning Network with Multi-scale Features
by Tian-Zi Niu, Zhen-Duo Chen, Xin Luo, Xin-Shun Xu
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel end-to-end encoder-decoder-based network (EVC-MF) for video captioning. Conventional approaches rely on offline-extracted features, which have limitations due to fixed parameters and limited adaptability to video caption datasets. The proposed EVC-MF efficiently utilizes multi-scale visual and textual features to generate video descriptions. It consists of three modules: a transformer-based network that updates feature extractor parameters, a masked encoder that reduces redundancy and learns useful features, and an enhanced transformer-based decoder that leverages shallow textual information. Experimental results on benchmark datasets demonstrate competitive performance compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to write captions for videos. Right now, people use different tools to get information from images or videos, but these tools have some big limitations. They were trained on certain types of tasks and don’t work well with other types of data, like video captioning datasets. This new approach uses a special kind of network that can learn from the video frames themselves and generate captions. It’s better than what we’re doing now because it can learn to be more flexible and get more useful information. The paper shows that this new approach works really well compared to other methods. |
Keywords
» Artificial intelligence » Decoder » Encoder » Encoder decoder » Transformer
