Using Large Language Models for Audio Descriptions in Egocentric Text-Audio Retrieval

Core Concepts

The author introduces a methodology using Large Language Models to generate audio-centric descriptions for egocentric text-audio retrieval, showing improved performance over original visual-centric descriptions.

Abstract

The study explores generating audio descriptions using LLMs for egocentric video settings, introducing new benchmarks and demonstrating the effectiveness of LLMs in improving text-audio retrieval. The research highlights the importance of leveraging audio information from video datasets and the potential of LLMs in enhancing search capabilities across different modalities.
Key points include:

Introduction of methodology using LLMs for audio-centric descriptions.
Creation of new benchmarks based on EpicMIR, EgoMCQ, and EpicSounds datasets.
Improved zero-shot performance with LLM-generated audio descriptions.
Utilization of few-shot learning to align vision and audio signals.
Application of LLMs to determine action associated with sound in videos.
Evaluation metrics like mAP and nDCG used for assessment.
The study showcases the significance of integrating audio information into text-audio retrieval tasks through innovative methodologies leveraging advanced language models.

Stats

"Our approach for obtaining audio-centric descriptions gives significantly higher zero-shot performance than using the original visual-centric descriptions."
"We show that using the same prompts, we can successfully employ LLMs to improve the retrieval on EpicSounds."
"LLMs can be used to determine the difficulty of identifying the action associated with a sound."

Quotes

"We introduce a methodology for generating audio-centric descriptions using Large Language Models (LLMs)."
"Our approach for obtaining audio-centric descriptions gives significantly higher zero-shot performance than using the original visual-centric descriptions."
"Finally, we confirm that LLMs can be used to determine the difficulty of identifying the action associated with a sound."

Key Insights Distilled From

A SOUND APPROACH

by Andr... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19106.pdf

Deeper Inquiries

How can integrating audio information enhance text-audio retrieval beyond egocentric settings?

Integrating audio information can significantly enhance text-audio retrieval by providing a more comprehensive understanding of the content. Beyond egocentric settings, this integration allows for a richer representation of multimedia data, enabling more accurate and detailed retrieval results. By incorporating audio cues, language models can capture nuances in sound that may not be evident from visual descriptions alone. This additional layer of information enhances the context and semantic understanding of the content being searched for, leading to improved retrieval accuracy and relevance.

What are potential limitations or biases introduced by relying on visual or audio-focused datasets when training language models?

Relying solely on visual or audio-focused datasets when training language models can introduce several limitations and biases. When using visual-centric datasets, there is a risk of overlooking important auditory cues that could provide valuable context for certain tasks. Similarly, with audio-focused datasets, critical visual details may be missed, leading to an incomplete understanding of the content. This unimodal training approach may result in models being biased towards one modality over the other, potentially affecting their performance on multimodal tasks where both modalities are essential.
Additionally, bias could arise from dataset imbalance between visual and audio samples within the training data. If one modality is overrepresented compared to the other, it may skew model predictions towards that dominant modality during inference. This imbalance could lead to suboptimal performance on tasks requiring equal consideration of both modalities.

How might advancements in multimodal understanding impact real-world applications beyond academic research?

Advancements in multimodal understanding have far-reaching implications across various real-world applications beyond academic research:

Enhanced User Experience: In fields like entertainment and gaming, improved multimodal systems can create more immersive experiences through better synchronization of visuals and sounds.

Accessibility: Multimodal technologies can benefit individuals with disabilities by providing alternative modes for interacting with digital content (e.g., voice commands for visually impaired users).

Healthcare: Applications in healthcare could leverage multimodal systems for diagnostics through analyzing patient records along with medical images or speech data.

Security & Surveillance: Enhanced surveillance systems combining video analysis with sound recognition capabilities offer advanced threat detection features.

Customer Insights: Businesses can utilize multimodal analytics to gain deeper insights into customer behavior by analyzing interactions across different channels like text feedback combined with call center recordings.

6Autonomous Vehicles: Multimodal sensors integrated into autonomous vehicles enable better perception capabilities by combining vision-based object detection with auditory alerts for enhanced safety measures.
These advancements underscore how leveraging multiple modalities simultaneously opens up new possibilities across diverse industries and use cases beyond traditional academic domains.