DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization
This addresses the need for efficient video browsing and retrieval on web platforms, though it appears incremental as it builds on existing query-aware summarization techniques.
The paper tackled the problem of query-aware multi-video summarization by introducing DeepQAMVS, a hierarchical pointer network that jointly optimizes conciseness, representativeness, and chronological soundness, achieving state-of-the-art results on the MVS1K dataset with linear inference time scaling.
The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content. Query-aware multi-video summarization is a promising technique that caters to this demand. In this work, we introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS, that jointly optimizes multiple criteria: (1) conciseness, (2) representativeness of important query-relevant events and (3) chronological soundness. We design a hierarchical attention model that factorizes over three distributions, each collecting evidence from a different modality, followed by a pointer network that selects frames to include in the summary. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.