Haagensen Slater (tempodomain38)

The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly.Recent research has witnessed advances in facial image editing tasks including face swapping and face reenactment. However, these methods are confined to dealing with one specific task at a time. In addition, for video facial editing, previous methods either simply apply transformations frame by frame or utilize multiple frames in a concatenated or iterative fashion, which leads to noticeable visual flickers. In this paper, we propose a unified temporally consistent facial video editing framework termed UniFaceGAN. Based on a 3D reconstruction model and a simple yet efficient dynamic training sample selection mechanism, our framework is designed to handle face swapping and face reenactment simultaneously. To enforce the temporal consistency, a novel 3D temporal loss constraint is introduced based on the barycentric coordinate interpolation. Besides, we propose a region-aware conditional normalization layer to replace the traditional AdaIN or SPADE to synthesize more context-harmonious results. Compared with the state-of-the-art facial image editing methods, our framework generates video portraits that are more photo-realistic and temporally smooth.Weakly supervised temporal action localization is a challenging task as only the video-level annotation is available during the training process. Immunology activator To address this problem, we propose a two-stage approach to generate high-quality frame-level pseudo labels by fully exploiting multi-resolution information in the temporal domain and complementary information between the appearance (i.e., RGB) and motion (i.e., optical flow) streams. In the first stage, we propose an Initial Label Generation (ILG) module to generate reliable initial frame-level pseudo labels. Specifically, in this newly proposed module, we exploit temporal multi-resolution consistency and cross-stream consistency to generate high quality class activation sequences (CASs), which consist of a number of sequences with each sequence measuring how likely each video frame belongs to one specific action class. In the second stage, we propose a Progressive Temporal Label Refinement (PTLR) framework to iteratively refine the pseudo labels, in which we use a set of selected frames with highly confident pseudo labels to progressively train two networks and better predict action class scores at each frame. Specifically, in our newly proposed PTLR framework, two networks called Network-OTS and Network-RTS, which are respectively used to generate CASs for the original temporal scale and the reduced temporal scales, are used as two streams (i.e., the OTS stream and the RTS stream) to refine the pseudo labels in turn. By this way, multi-resolution information in the temporal domain is exchanged at the pseudo label level, and our work can help improve each network/stream by exploiting the refined pseudo labels from another network/stream. Comprehensive experiments on two benchmark datasets THUMOS14 and ActivityNet v1.3 demonstrate the effectiveness of our newly proposed method for weakly supervised temporal action localization.Cavitation is the fundamental physical mechanism of various focused ultrasound (FUS)-mediated therapies in the brain. Accurately knowing the 3D location of cavitation in real-time can improve the targeting accuracy and avoid off-target tissue damage. Existing techniques for 3D passive transcranial cavitation detection require the use of expensive