Learning Language-Visual Embedding for Movie Understanding with Natural-Language
This work addresses movie understanding for applications like video annotation and retrieval, but it is incremental as it builds on existing embedding methods with new data and tasks.
The paper tackled the problem of learning joint language-visual embeddings for movie understanding by evaluating three neural network architectures on the LSMDC16 dataset, achieving a Recall@10 of 19.2% for annotation and 18.9% for retrieval on a subset, and 58.11% accuracy on a multiple-choice test.
Learning a joint language-visual embedding has a number of very appealing properties and can result in variety of practical application, including natural language image/video annotation and search. In this work, we study three different joint language-visual neural network model architectures. We evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1) Standard Ranking for video annotation and retrieval 2) Our proposed movie multiple-choice test. This test facilitate automatic evaluation of visual-language models for natural language video annotation based on human activities. In addition to original Audio Description (AD) captions, provided as part of LSMDC16, we collected and will make available a) manually generated re-phrasings of those captions obtained using Amazon MTurk b) automatically generated human activity elements in "Predicate + Object" (PO) phrases based on "Knowlywood", an activity knowledge mining model. Our best model archives Recall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subset of 1000 samples. For multiple-choice test, our best model achieve accuracy 58.11% over whole LSMDC16 public test-set.