Summary of Contextiq: a Multimodal Expert-based Video Retrieval System For Contextual Advertising, by Ashutosh Chaubey et al.
ContextIQ: A Multimodal Expert-Based Video Retrieval System for Contextual Advertising
by Ashutosh Chaubey, Anoubhav Agarwaal, Sartaki Sinha Roy, Aayush Agrawal, Susmita Ghose
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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 ContextIQ, a multimodal expert-based video retrieval system designed specifically for contextual advertising. The system utilizes modality-specific experts-video, audio, transcript (captions), and metadata such as objects, actions, emotion, etc.-to create semantically rich video representations. This allows for effective contextual advertising, which requires understanding complex video content at a granular level. ContextIQ achieves better or comparable results to state-of-the-art models on multiple text-to-video retrieval benchmarks without joint training. The paper also explores the benefits of leveraging multiple modalities for enhanced video retrieval accuracy and discusses its applications in an ad ecosystem while addressing concerns related to brand safety and filtering inappropriate content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Contextual advertising shows ads that match what you’re watching. This is important because more people are watching videos online, and they want to see relevant ads. For this to work, we need a system that can understand complex video content very well. Current systems based on text-to-video training require lots of data and computer power, making them hard to use. The paper introduces ContextIQ, a new system that uses multiple experts (video, audio, captions, metadata) to create rich video representations. This helps find the right ad for the right context, leading to better ad experiences and more money for advertisers. |
