Summary of Multimodal Contextualized Support For Enhancing Video Retrieval System, by Quoc-bao Nguyen-le et al.
Multimodal Contextualized Support for Enhancing Video Retrieval System
by Quoc-Bao Nguyen-Le, Thanh-Huy Le-Nguyen
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 A new approach is proposed for video retrieval systems to better handle queries describing actions or events over multiple frames. Current methods focus on querying individual images or keyframes, which can lead to inaccurate results when analyzing a single frame. The authors argue that extracting embeddings solely from images does not provide enough information for models to capture higher-level insights inferred from the video. A novel pipeline is introduced that integrates multimodal data and incorporates information from multiple frames within a video, enabling the model to abstract higher-level information and focus on what can be inferred from the video clip rather than just object detection in one single image. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video retrieval systems are used to find specific videos based on user queries. Currently, these systems are not very good at handling queries that describe an action or event that happens over several frames of a video. Instead, they focus on individual images or keyframes, which can lead to inaccurate results. The authors propose a new approach that uses information from multiple frames in a video to better understand what the user is looking for. |
Keywords
» Artificial intelligence » Object detection
