Test-Time Adaptation for Video Highlight Detection Using Meta-Auxiliary Learning and Cross-Modality Hallucinations

2025-08-06

AI Summary

Existing video highlight detection methods rely on static models, exhibiting poor generalization to unseen test videos with diverse content, stylistic variations, and heterogeneous audiovisual quality. To address this limitation, we propose Highlight-TTA—the first test-time adaptation (TTA) framework for video highlight detection. Highlight-TTA jointly optimizes the primary highlight detection task and a meta-auxiliary cross-modal hallucination reconstruction task, enabling dynamic adaptation to each test video’s unique characteristics during inference. Crucially, it requires no additional annotations or external training data, achieving lightweight online adaptation via a single forward-backward pass. Extensive experiments demonstrate consistent and significant performance gains across three state-of-the-art highlight detection models and three benchmark datasets, validating its generality, effectiveness, and plug-and-play compatibility.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Existing video highlight detection methods, although advanced, struggle to generalize well to all test videos. These methods typically employ a generic highlight detection model for each test video, which is suboptimal as it fails to account for the unique characteristics and variations of individual test videos. Such fixed models do not adapt to the diverse content, styles, or audio and visual qualities present in new, unseen test videos, leading to reduced highlight detection performance. In this paper, we propose Highlight-TTA, a test-time adaptation framework for video highlight detection that addresses this limitation by dynamically adapting the model during testing to better align with the specific characteristics of each test video, thereby improving generalization and highlight detection performance. Highlight-TTA is jointly optimized with an auxiliary task, cross-modality hallucinations, alongside the primary highlight detection task. We utilize a meta-auxiliary training scheme to enable effective adaptation through the auxiliary task while enhancing the primary task. During testing, we adapt the trained model using the auxiliary task on the test video to further enhance its highlight detection performance. Extensive experiments with three state-of-the-art highlight detection models and three benchmark datasets show that the introduction of Highlight-TTA to these models improves their performance, yielding superior results.

Problem

• Improving generalization in video highlight detection
• Adapting models to diverse video content dynamically
• Enhancing performance via meta-auxiliary learning and cross-modality hallucinations

Innovation

• Test-time adaptation for video highlight detection
• Meta-auxiliary learning enhances model adaptation
• Cross-modality hallucinations improve generalization