Augmenting Moment Retrieval: Zero-Dependency Two-Stage Learning

2025-10-22

AI Summary

Existing moment retrieval methods suffer from scarce annotated data, ambiguous temporal boundaries, and weak discriminative capability for fine-grained action semantics (e.g., “kicking a ball” vs. “throwing a ball”). To address these challenges, we propose AMR—a zero-external-dependency enhancement framework that operates in two stages: cold-start training followed by self-distillation. AMR jointly leverages original and actively generated queries to automatically rectify boundary ambiguities and semantic noise without additional annotations. We introduce curriculum learning, cross-stage distillation loss, and a model distillation strategy that freezes the base query encoder—thereby enhancing temporal localization discrimination and robustness to real-world data distributions. Extensive experiments demonstrate that AMR significantly outperforms state-of-the-art methods across multiple benchmarks, validating its efficacy in low-dependency settings, efficient learning, and strong generalization.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Active Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Existing Moment Retrieval methods face three critical bottlenecks: (1) data scarcity forces models into shallow keyword-feature associations; (2) boundary ambiguity in transition regions between adjacent events; (3) insufficient discrimination of fine-grained semantics (e.g., distinguishing ``kicking" vs. ``throwing" a ball). In this paper, we propose a zero-external-dependency Augmented Moment Retrieval framework, AMR, designed to overcome local optima caused by insufficient data annotations and the lack of robust boundary and semantic discrimination capabilities. AMR is built upon two key insights: (1) it resolves ambiguous boundary information and semantic confusion in existing annotations without additional data (avoiding costly manual labeling), and (2) it preserves boundary and semantic discriminative capabilities enhanced by training while generalizing to real-world scenarios, significantly improving performance. Furthermore, we propose a two-stage training framework with cold-start and distillation adaptation. The cold-start stage employs curriculum learning on augmented data to build foundational boundary/semantic awareness. The distillation stage introduces dual query sets: Original Queries maintain DETR-based localization using frozen Base Queries from the cold-start model, while Active Queries dynamically adapt to real-data distributions. A cross-stage distillation loss enforces consistency between Original and Base Queries, preventing knowledge forgetting while enabling real-world generalization. Experiments on multiple benchmarks show that AMR achieves improved performance over prior state-of-the-art approaches.

Problem

• Overcoming data scarcity in moment retrieval models
• Resolving boundary ambiguity between adjacent events
• Enhancing fine-grained semantic discrimination capabilities

Innovation

• Zero-dependency framework resolves annotation ambiguities without extra data
• Two-stage training combines curriculum learning with distillation adaptation
• Dual query sets maintain localization while enabling dynamic real-world adaptation