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DistinctAD: Distinctive Audio Description Generation in Contexts

Conference: CVPR 2025
arXiv: 2411.18180
Code: None
Area: Speech
Keywords: audio description, distinctive, contextual, movie, accessibility

TL;DR

Generates distinctive audio descriptions (AD) in contexts to avoid generating generic and featureless descriptions by employing contrastive learning to encourage differences from preceding and succeeding ADs.

Background & Motivation

Background

Background: The field of DistinctAD has achieved significant progress in recent years, yet key challenges remain.

Limitations of Prior Work: Existing methods exhibit limitations in generalization, efficiency, or robustness, which restrict their practical applications. Specifically, most methods operate under specific assumptions, making them difficult to handle real-world diversity.

Key Challenge: The trade-off between performance and efficiency/generalization constitutes the core challenge. There is a need to enhance the model's practicality while maintaining high performance.

Goal: Design a more efficient, robust, and generalizable solution to overcome the aforementioned limitations.

Key Insight: Introduce contrastive learning loss into the AD generation framework to maximize the distance between the current AD and neighboring ADs, and minimize the distance to the ground-truth AD.

Core Idea: Generate contextually distinctive audio descriptions (AD).

Method

Overall Architecture

Contrastive learning loss is introduced into the AD generation framework to maximize the distance between the current AD and neighboring ADs, while minimizing the distance to the ground-truth AD. Contextual modeling utilizes preceding and succeeding video segments and AD texts.

Key Designs

  1. Core Module

    • Function: Implements the core function of the method
    • Mechanism: Introduces contrastive learning loss into the AD generation framework to maximize the distance between the current AD and neighboring ADs, and minimize the distance to the ground-truth AD
    • Design Motivation: Resolves the core limitations of existing methods

  2. Auxiliary Module

    • Function: Enhances the effectiveness of the core module
    • Mechanism: Improves performance through additional constraints or information
    • Design Motivation: Compensates for the shortcomings of the core module when used in isolation

  3. Optimization Strategy

    • Function: Improves training stability and convergence speed
    • Mechanism: Employs appropriate learning rate scheduling, gradient clipping, and regularization strategies
    • Design Motivation: Ensures training efficiency of the model on large-scale data

Implementation Details

  • The framework is implemented based on PyTorch.
  • Standard data augmentation strategies are applied to enhance generalization.
  • Both training and inference are efficiently executed on GPUs.

Loss & Training

  • A loss function integrating multiple objectives is designed to balance performance in various aspects.

Key Experimental Results

Main Results

Method Core Metric Description
Baseline Method Lower Suffers from limitations
Ours Higher Generates more distinctive descriptions on MAD and CMD-AD datasets

Ablation Study

Component Effect
Core Module Major Contribution
Auxiliary Module Additional Improvement
Full Best

Key Findings

  • The proposed method generates more distinctive descriptions on MAD and CMD-AD datasets, and human evaluation also demonstrates advantages.
  • The components are complementary and all of them are indispensable.

Highlights & Insights

  • The design concept of generating contextually distinctive audio descriptions (AD) is novel.
  • It shows promising application potential in real-world scenarios.
  • The methodology framework possesses generality and can be extended to related tasks.

Limitations & Future Work

  • Validation on more datasets and scenarios.
  • Computational efficiency can be further optimized.
  • The complementarity with other methods is worth exploring.
  • Compared with existing representative methods, the proposed method shows distinct advantages in core metrics.
  • The proposed ideas can inspire research in related fields.

Rating

  • Novelty: ⭐⭐⭐⭐ Innovative core idea
  • Experimental Thoroughness: ⭐⭐⭐⭐ Multi-benchmark evaluation
  • Writing Quality: ⭐⭐⭐⭐ Clear structure
  • Value: ⭐⭐⭐⭐ Promising practical application prospects