Zero-Shot Video Event Detection with High-Order Semantic Concept Discovery and Matching
- Publisher:
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Multimedia, 2022, 24, pp. 1896-1908
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Zero-Shot_Video_Event_Detection_With_High-Order_Semantic_Concept_Discovery_and_Matching.pdf | Published version | 4.32 MB |
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Multimedia event detection aims to precisely retrieve videos that contain complex semantic events from a large pool. This work addresses this task under a zero-shot setting, where only brief event-specific textural information (such as event names, a few descriptive sentences, etc.) is known yet none positive video example is provided. Mainstream approaches to tackling this task are middle-level semantic concept-based, where meticulously-crafted concept banks (e.g., LSCOM) are adopted. We argue that these concept banks are still inadequate facing video semantic complexity. Existing semantic concepts are essentially first-order, mainly designed for atomic objects, scenes or human actions, etc. This work advocates the utilization of high-order concepts (such as subject-predicate-object triplets or adjective-object). The main contributions are two-fold. First, we harvest a comprehensive albeit compact high-order concept library through distilling information from three large public datasets (MS-COCO, Visual Genome, and Kinetics-600), mainly related to visual relations and human-object interactions. Secondly, zero-shot events are often only briefly and partially described via textual input. The resultant semantic ambiguity makes the pursuit of the most indicative high-order concepts challenging. We thus design a novel query-expanding scheme that enriches ambiguous event-specific keywords by searching over either large common knowledge bases (e.g., WikiHow) or top-ranked webpages retrieved from modern search engines. This way sets up a more faithful connection between zero-shot events and high-order concepts. To our best knowledge, this is the first work that strives for concept-based video search beyond first-order concepts. Extensive experiments have been conducted on several large video benchmarks (TRECVID 2013, TRECVID 2014, and ActivityNet-1.3). The evaluations clearly demonstrate the superiority of our constructed high-order concept library and its complementariness to existing concepts.
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