Video Object Grounding using Semantic Roles in Language Description
Table 3: Comparison of VOGNet against ImgGrnd and VidGrnd. GT5 and P100 use 5 and 100 proposals per frame. Here, Acc: Grounding Accuracy, VAcc: Video accuracy, Cons: Consistency, SAcc: Strict Accuracy (see Section 4.3 for details). On the challenging evaluation metrics of TEMP and SPAT, VOGNet (ours) shows significant improvement over competitive image and video grounding baselines.
['[EMPTY]', 'Model', 'SVSQ Acc', 'SVSQ SAcc', 'SEP Acc', 'SEP VAcc', 'SEP SAcc', 'TEMP Acc', 'TEMP VAcc', 'TEMP Cons', 'TEMP SAcc', 'SPAT Acc', 'SPAT VAcc', 'SPAT Cons', 'SPAT SAcc']
across GT5 (5 proposal boxes per frame) and P100 (100 proposal boxes per frame). In practice, SPAT and TEMP strategies when applied to contrastive videos from ActivityNet are effective proxies to obtaining naturally occurring contrastive examples from the web.