Summary of Reversed in Time: a Novel Temporal-emphasized Benchmark For Cross-modal Video-text Retrieval, by Yang Du et al.
Reversed in Time: A Novel Temporal-Emphasized Benchmark for Cross-Modal Video-Text Retrieval
by Yang Du, Yuqi Liu, Qin Jin
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 introduces RTime, a novel temporal-emphasized video-text retrieval dataset that poses new challenges to video-text retrieval models. The dataset is created by obtaining videos of actions or events with significant temporality, reversing these videos to create harder negative samples, and annotating the significance and reversibility of candidate videos. The authors also propose three retrieval benchmark tasks: RTime-Origin, RTime-Hard, and RTime-Binary. They use GPT-4 to extend human-written captions for qualified videos, resulting in a dataset consisting of 21k videos with 10 captions per video, totaling about 122 hours. RTime is designed to assess the abilities of models in temporal understanding, which makes video-text retrieval more challenging than image-text retrieval. The authors find that widely used video-text benchmarks have shortcomings in comprehensively assessing model performance, particularly in temporal understanding. Large-scale image-text pre-trained models can already achieve comparable zero-shot performance with video-text pre-trained models. To address this issue, the authors propose using harder-negatives in model training and benchmarking a variety of video-text models on RTime. The paper demonstrates that RTime indeed poses new and higher challenges to video-text retrieval through extensive experiment analysis. The authors release their RTime dataset to further advance video-text retrieval and multimodal understanding research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new dataset called RTime for video-text retrieval, which is more challenging than image-text retrieval because it requires temporal understanding. The dataset has 21k videos with captions, and it’s designed to test models’ abilities in understanding the timing of actions or events. The authors found that existing benchmarks are not good at measuring model performance, especially when it comes to temporal understanding. They propose using a new approach to train models and benchmark them on this new dataset. The paper shows that RTime is a harder task than existing ones, and it requires more advanced models to achieve good results. The authors release their dataset so that other researchers can use it and improve video-text retrieval. |
Keywords
* Artificial intelligence * Gpt * Zero shot
