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Given a language query, temporal grounding task is to localize temporal boundaries of the described event in an untrimmed video. There is a long-standing challenge that multiple moments may be associated with one same video-query pair, termed label uncertainty. However, existing methods struggle to localize diverse moments due to the lack of multi-label annotations. In this paper, we propose a novel Diverse Temporal Grounding framework (DTG) to achieve diverse moment localization with only single-label annotations. By delving into the label uncertainty, we find the diverse moments retrieved tend to involve similar actions/objects, driving us to perceive these interest moments. Specifically, we construct soft multi-label through semantic similarity of multiple video-query pairs. These soft labels reveal whether multiple moments in the intra-videos contain similar verbs/nouns, thereby guiding interest moment generation. Meanwhile, we put forward a diverse moment regression network (DMRNet) to achieve multiple predictions in a single pass, where plausible moments are dynamically picked out from the interest moments for joint optimization. Moreover, we introduce new metrics that better reveal multi-output performance. Extensive experiments conducted on Charades-STA and ActivityNet Captions show that our method achieves state-of-the-art performance in terms of both standard and new metrics. [ABSTRACT FROM AUTHOR]

Visual relationship detection, as a challenging task used to find and distinguish interactions between object-pairs in one image, has received much attention recently. In this work, we devise a unified visual relationship detection framework with two types of correlation exploitation to address the combination explosion problem in the object-pairs proposing stage and the non-exclusive label problem in the predicate recognition stage. In the object-pairs proposing stage, with the exploitation of relative location correlation between two objects in one pair, one location-embedded rating module (LRM) is developed to effectively select plausible proposals. In the predicate recognition stage, one label-correlation graph module (LGM) is introduced to measure the implicit semantic correlation among predicates; and then assign discrete distributed labels to predicates to improve the precision of top-n recall. Experiments on the two widely used VRD and VG datasets show that our proposed method outperforms current state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Hashing is a widely adopted method based on an approximate nearest neighbor search and is used in large-scale image retrieval tasks. Conventional learning-based hashing algorithms employ end-to-end representation learning, which is a one-off technique. Because of the tradeoff between efficiency and performance, conventional learning-based hashing methods must sacrifice code length to improve performance, which increases their computational complexity. To improve the efficiency of binary codes, motivated by the “nonsalient-to-salient” attention scheme of humans, we propose a recursive hashing mechanism that maps progressively expanded salient regions to a series of binary codes. These salient regions are generated by a conventional saliency model based on bottom-up saliency-driven attention and a semantic-guided saliency model based on top-down task-driven attention. After obtaining a series of salient regions, we perform long-range temporal modeling of salient regions using a graph-based recurrent deep network to obtain more refined representative features. The later output nodes inherit aggregated information from all previous nodes and extract discriminative features from more salient regions. Therefore, this network possesses more significant information and satisfactory scalability. The proposed recursive hashing neural network, optimized by a triplet ranking loss, is end-to-end trainable. Extensive experimental results from several image retrieval benchmarks show the scalability of our method and demonstrate its strong performance compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]