Summary of Vidla: Video-language Alignment at Scale, by Mamshad Nayeem Rizve et al.
VidLA: Video-Language Alignment at Scale
by Mamshad Nayeem Rizve, Fan Fei, Jayakrishnan Unnikrishnan, Son Tran, Benjamin Z. Yao, Belinda Zeng, Mubarak Shah, Trishul Chilimbi
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 proposes VidLA, an approach for video-language alignment at scale, addressing two major limitations of previous approaches. The first limitation is the inability to capture both short-range and long-range temporal dependencies using complex hierarchical deep network architectures that are hard to integrate with existing pretrained image-text foundation models. To overcome this, VidLA employs a simple two-tower architecture that initializes its video-language model with these foundation models, boosting performance. The second limitation is the lack of semantically aligned large-scale training data, which VidLA addresses by leveraging recent language models to curate the largest video-language dataset to date with better visual grounding. This dataset includes video clips of varying durations, aiding in extracting better representations at different temporal scales. Empirical results show that VidLA surpasses state-of-the-art methods on multiple retrieval benchmarks and performs competitively on classification benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers understand videos better. Right now, it’s hard for computers to match what’s happening in a video with what people are saying about it. The researchers propose a new way to do this called VidLA, which uses simple ideas instead of complicated computer architectures. This makes it easier to connect the computer’s understanding of images and text together. The other big problem is that there isn’t enough data for computers to learn from. To fix this, the researchers created a huge dataset of videos and words that match what people are saying about them. They tested VidLA on different tasks and found that it does better than other methods. |
Keywords
* Artificial intelligence * Alignment * Boosting * Classification * Grounding * Language model
