Dilation-Erosion for Single-Frame Supervised Temporal Action Localization

To balance the annotation labor and the granularity of supervision, single-frame annotation has been introduced in temporal action localization. It provides a rough temporal location for an action but implicitly overstates the supervision from the annotated-frame during training, leading to the confusion between actions and backgrounds, i.e., action incompleteness and background false positives. To tackle the two challenges, in this work, we present the Snippet Classification model and the Dilation-Erosion module. In the Dilation-Erosion module, we expand the potential action segments with a loose criterion to alleviate the problem of action incompleteness and then remove the background from the potential action segments to alleviate the problem of action incompleteness. Relying on the single-frame annotation and the output of the snippet classification, the Dilation-Erosion module mines pseudo snippet-level ground-truth, hard backgrounds and evident backgrounds, which in turn further trains the Snippet Classification model. It forms a cyclic dependency. Furthermore, we propose a new embedding loss to aggregate the features of action instances with the same label and separate the features of actions from backgrounds. Experiments on THUMOS14 and ActivityNet 1.2 validate the effectiveness of the proposed method. Code has been made publicly available (https://github.com/LingJun123/single-frame-TAL).Comment: 28 pages, 8 figure

The study of lepton EDM in CP violating BLMSSM

In the supersymmetric model with local gauged baryon and lepton numbers(BLMSSM), the CP violating effects are considered to study the lepton electric dipole moment(EDM). The CP violating phases in BLMSSM are more than those in the standard model(SM) and can give large contributions. The analysis of the EDMs for the leptons $e,\mu, \tau$ is shown in this work. It is in favour of exploring the source of CP violation and probing the physics beyond SM.Comment: 20 pages, 10figure