Learning Temporal Co-Attention Models for Unsupervised Video Action Localization
Guoqiang Gong,  Xinghan Wang,  Yadong Mu,  Qi Tian

Temporal action localization (TAL) in untrimmed videos recently receives tremendous research enthusiasm. To our best knowledge, this is the first attempt in the literature to explore this task under an unsupervised setting, hereafter referred to as action co-localization (ACL), where only the total count of unique actions that appear in the video set is known. To solve ACL, we propose a two-step "clustering + localization" iterative procedure. The clustering step provides noisy pseudo-labels for the localization step, and the localization step provides temporal co-attention models that in turn improve the clustering performance. Using such two-step procedure, weakly-supervised TAL can be regarded as a direct extension of our ACL model. Technically, our contributions are two-folds: 1) temporal co-attention models, either class-specific or class-agnostic, learned from video-level labels or pseudo-labels in an iterative reinforced fashion; 2) new losses specially designed for ACL, including action-background separation loss and cluster-based triplet loss. Comprehensive evaluations are conducted on 20-action THUMOS14 and 100-action ActivityNet-1.2. On both benchmarks, the proposed model for ACL exhibits strong performances, even surprisingly comparable with state-of-the-art weakly-supervised methods. For example, previous best weakly-supervised model achieves 26.8% under mAP@0.5 on THUMOS14, our new records are 30.1% (weakly-supervised) and 25.0% (unsupervised).