Zero-shot Video Classification with Appropriate Web and Task Knowledge Transfer

Abstract

Zero-shot video classification (ZSVC) that aims to recognize video classes that have never been seen during model training, has become a thriving research direction. ZSVC is achieved by building mappings between visual and semantic embeddings. Recently, ZSVC has been achieved by automatically mining the underlying objects in videos as attributes and incorporating external commonsense knowledge. However, the object mined from seen categories can not generalized to unseen ones. Besides, the category-object relationships are usually extracted from commonsense knowledge or word embedding, which is not consistent with video modality. To tackle these issues, we propose to mine associated objects and category-object relationships for each category from retrieved web images. The associated objects of all categories are employed as generic attributes and the mined category-object relationships could narrow the modality inconsistency for better knowledge transfer. Another issue of existing ZSVC methods is that the model sufficiently trained with labeled seen categories may not generalize well to distinct unseen categories. To encourage a more reliable transfer, we propose Task Similarity aware Representation Learning (TSRL). In TSRL, the similarity between seen categories and the unseen ones is estimated and used to regularize the model in an appropriate way. We construct a model for ZSVC based on the constructed attributes, the mined category-object relationships and the proposed TSRL. Experimental results on four public datasets, i.e., FCVID, UCF101, HMDB51 and Olympic Sports, show that our model performs favorably against state-of-the-art methods. Our codes are publicly available at https://github.com/junbaoZHUO/TSRL.