In this paper, we tackle the problem of retrieving videos using complex natural language queries. Towards this goal, we first parse the sentential descriptions into a semantic graph, which is then matched to visual concepts using a generalized bipartite matching algorithm. Our approach exploits object appearance, motion and spatial relations, and learns the importance of each term using structure prediction. We demonstrate the effectiveness of our approach on a new dataset designed for semantic search in the context of autonomous driving, which exhibits complex and highly dynamic scenes with many objects. We show that our approach is able to locate a major portion of the objects described in the query with high accuracy, and improve the relevance in video retrieval.
As for video retrieval, Lin et al. REF parsed the sentence descriptions into a semantic graph, which are then matched to visual concepts in the videos by generalized bipartite matching.
Visual Semantic Search: Retrieving Videos via Complex Textual Queries
2014 IEEE Conference on Computer Vision and Pattern Recognition
2014 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science