Summary of Context-aware Temporal Embedding Of Objects in Video Data, by Ahnaf Farhan and M. Shahriar Hossain
Context-Aware Temporal Embedding of Objects in Video Data
by Ahnaf Farhan, M. Shahriar Hossain
First submitted to arxiv on: 23 Aug 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 The proposed model constructs context-aware temporal object embeddings by leveraging adjacency and semantic similarities between objects from neighboring video frames. Unlike traditional methods, this approach considers contextual relationships between objects, creating a meaningful embedding space where temporally connected objects’ vectors are positioned in proximity. The model is demonstrated to enhance the effectiveness of downstream applications when used with conventional visual embeddings, and can also be used to narrate a video using a Large Language Model (LLM). The proposed objective function generates context-aware temporal object embeddings for video data, showcasing potential applications in video analysis and object classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to understand videos by looking at the relationships between objects over time. Instead of just focusing on what objects look like, this model considers how objects are connected in different parts of the video. This helps create a better understanding of what’s happening in the video and can be used to improve object classification and video analysis tasks. The model is also shown to be useful for creating summaries of videos, using large language models. |
Keywords
» Artificial intelligence » Classification » Embedding space » Large language model » Objective function
