OVEL: Large Language Model for Online Video Entity Linking

Concepts de base

The author proposes the OVEL task to link entities in online videos, introduces the LIVE dataset, and combines a Large Language Model with a retrieval model for efficient memory management.

Résumé

The paper introduces the OVEL task focusing on live video streams, proposes the LIVE dataset, and presents a method combining LLM with a retrieval model. Experimental results show the effectiveness of the approach.
In recent years, multi-modal entity linking has gained attention due to its significance in various applications. Most existing methods focus on linking textual and visual mentions but overlook online video content. The proposed OVEL task aims to establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness. To facilitate this research, a live delivery entity linking dataset called LIVE is constructed. An evaluation metric considering timelessness, robustness, and accuracy is introduced.
Videos have become dominant for communication, leading to increased academic research into understanding them. Specific entities within videos are crucial for viewers seeking detailed information. Video entity linking refers to linking mentions in videos to corresponding entities in a knowledge base. Existing studies primarily focus on static visual-textual pairs or coarse-grained entities without real-time processing demands.
The paper proposes an innovative framework that combines a Large Language Model with a retrieval model for efficient memory management in online video entity linking tasks. Experimental results demonstrate the effectiveness of this approach compared to traditional methods.
Challenges of the OVEL task include managing noise in real-time scenarios, ensuring timeliness, and requiring domain-specific knowledge for accurate identifications. Methodologies proposed address these challenges by leveraging LLM-based information extraction, utilizing memory blocks for real-time processing, and employing retrieval augmentation for domain-specific scenarios.
The main contributions of the paper include introducing the OVEL task focusing on improving accuracy and efficiency of entity recognition in online videos, creating the LIVE dataset for live stream product recognition, proposing a framework based on LLM as a memory manager for comprehensive video stream information management.

Stats

82 live stream videos obtained from Taobao Live
Average duration of 5.6 hours per livestream video
Average of 51.3 live product items per livestream video

Citations

"Consider a live streaming example: where a video captioning model might merely state “a host explaining a product”, however, for viewers specific details like “Nike Air Jordan 37th Generation Mid-Top Basketball Shoes” might be critical information they seek."
"In most cases using single memory management approach yields slightly inferior results compared to using full summaries as structure lacks complete information leading to some loss."
"Our method achieves best performance once again demonstrating effectiveness."

Idées clés tirées de

OVEL

by Haiquan Zhao... à arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01411.pdf

Questions plus approfondies

How can the proposed method be adapted or extended to handle different types of multimedia content beyond just videos?

The proposed method can be adapted to handle different types of multimedia content by incorporating additional modalities such as images, audio, and text. For image data, the retrieval model can be modified to extract features from images and combine them with textual information for entity linking. Similarly, for audio data, speech-to-text conversion can be integrated into the system to process spoken content. By extending the framework to include these modalities, a more comprehensive understanding of multimedia content can be achieved.

What potential limitations or biases could arise from relying heavily on large language models like LLMs for memory management?

Relying heavily on large language models (LLMs) for memory management may introduce several limitations and biases. One potential limitation is related to domain specificity - LLMs trained on general corpora may not have specialized knowledge required for certain domains, leading to inaccuracies in memory storage and retrieval. Additionally, biases present in the training data of LLMs could propagate into memory management decisions, potentially reinforcing existing biases in entity linking tasks. Moreover, scalability issues may arise when dealing with large volumes of data due to resource constraints associated with LLMs.

How might advancements in multimodal entity linking impact industries such as e-commerce or digital marketing?

Advancements in multimodal entity linking have significant implications for industries like e-commerce and digital marketing. In e-commerce, improved entity linking capabilities enable better product recommendations based on detailed analysis of multimedia content such as videos and images. This leads to enhanced customer experience through personalized suggestions tailored to individual preferences. In digital marketing, accurate identification of entities within multimedia content allows marketers to target specific audiences more effectively by aligning products with consumer interests expressed across various modalities. Overall, advancements in multimodal entity linking empower businesses in these industries to optimize their strategies and drive higher engagement levels among consumers.