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VRFP is a real-time video retrieval framework based on short text inputqueries, which obtains weakly labeled training images from the web after thequery is known. The retrieved web images representing the query and eachdatabase video are treated as unordered collections of images, and eachcollection is represented using a single Fisher Vector built on CNN features.Our experiments show that a Fisher Vector is robust to noise present in webimages and compares favorably in terms of accuracy to other standardrepresentations. While a Fisher Vector can be constructed efficiently for a newquery, matching against the test set is slow due to its high dimensionality. Toperform matching in real-time, we present a lossless algorithm that acceleratesthe inner product computation between high dimensional Fisher Vectors. We provethat the expected number of multiplications required decreases quadraticallywith the sparsity of Fisher Vectors. We are not only able to construct andapply query models in real-time, but with the help of a simple re-rankingscheme, we also outperform state-of-the-art automatic retrieval methods by asignificant margin on TRECVID MED13 (3.5%), MED14 (1.3%) and CCV datasets(5.2%). We also provide a direct comparison on standard datasets between twodifferent paradigms for automatic video retrieval - zero-shot learning andon-the-fly retrieval.

VRFP: On-the-fly Video Retrieval using Web Images and Fast Fisher Vector Products