MELON: A Large-Scale Dataset for Multi-Event Text-to-Long-Video Retrieval

• URL: http://arxiv.org/abs/2609.01654v1
• Date: Mon, 31 Aug 2026 08:20:35 GMT
• Title: MELON: A Large-Scale Dataset for Multi-Event Text-to-Long-Video Retrieval
• Abstract summary: Existing text-video retrieval datasets primarily consist of short-form clips containing a single dominant event.<n>We introduce MELON, the first large-scale dataset designed to extend text-video retrieval to long-form videos featuring complex, multi-event structures.
• Score: 9.116098272868667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing text-video retrieval datasets primarily consist of short-form clips containing a single dominant event. While suitable for measuring basic vision-language alignment, they are limited in capturing real-world retrieval scenarios, where long-form videos naturally contain multiple semantically distinct events and a single text query may correspond to several non-contiguous temporal segments. To bridge this gap, we introduce MELON, the first large-scale dataset designed to extend text-video retrieval to long-form videos featuring complex, multi-event structures. MELON explicitly annotates multiple event intervals per video along with their corresponding textual descriptions, enabling both training and evaluation of multi-event understanding in long, untrimmed videos. In addition, we propose a multi-event aware loss that encourages models to differentiate between full-event and partial-event matches, yielding substantial improvements in retrieval accuracy. Together, the MELON dataset and our proposed loss establish a robust foundation for expanding text-to-video retrieval to complex long-form scenarios and provide a more realistic evaluation setting for future research in the field.

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