Summary of Unveiling the Invisible: Captioning Videos with Metaphors, by Abisek Rajakumar Kalarani et al.
Unveiling the Invisible: Captioning Videos with Metaphors
by Abisek Rajakumar Kalarani, Pushpak Bhattacharyya, Sumit Shekhar
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a new Vision-Language (VL) task that involves describing metaphors present in videos. The authors construct a manually created dataset with 705 videos and 2115 human-written captions, along with a novel metric called Average Concept Distance (ACD) to evaluate the creativity of generated metaphors. They also propose a low-resource video metaphor captioning system, GIT-LLaVA, which achieves comparable performance to state-of-the-art (SoTA) video language models on the proposed task. The paper provides a comprehensive analysis of existing video language models and publishes its dataset, models, and benchmark results to facilitate further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how computers can understand metaphors in videos. Right now, most computer models struggle with understanding visual metaphors like those used in memes or ads. Researchers have tried to teach these models to recognize metaphors in text form, but no one has studied whether they can do the same for metaphors in video. To fix this problem, the authors create a new dataset of 705 videos and their corresponding captions, along with a special way to measure how creative the generated captions are. They also develop a system that can caption videos using metaphors, which performs similarly to other top-performing systems. |
