Blalock Klemmensen (outputnight51)

To learn compact and discriminative representations, we further propose a novel temporal resolution-aware regularization, which enforces the consistency among different temporal resolutions for the same identities. We conduct extensive evaluations on four benchmarks, i.e. iLIDS-VID, PRID2011, MARS, and DukeMTMC-VideoReID, experimental results achieve the competitive performance which demonstrates the effectiveness of our proposed method. Code is available at https//github.com/weleen/AGRL.pytorch.Visible thermal person re-identification (VT-ReID) is a challenging cross-modality pedestrian retrieval problem due to the large intra-class variations and modality discrepancy across different cameras. Existing VT-ReID methods mainly focus on learning cross-modality sharable feature representations by handling the modality-discrepancy in feature level. However, the modality difference in classifier level has received much less attention, resulting in limited discriminability. In this paper, we propose a novel modality-aware collaborative ensemble (MACE) learning method with middle-level sharable two-stream network (MSTN) for VT-ReID, which handles the modality-discrepancy in both feature level and classifier level. In feature level, MSTN achieves much better performance than existing methods by capturing sharable discriminative middlelevel features in convolutional layers. In classifier level, we introduce both modality-specific and modality-sharable identity classifiers for two modalities to handle the modality discrepancy. To utilize the complementary information among different classifiers, we propose an ensemble learning scheme to incorporate the modality sharable classifier and the modality specific classifiers. In addition, we introduce a collaborative learning strategy, which regularizes modality-specific identity predictions and the ensemble outputs. Extensive experiments on two cross-modality datasets demonstrate that the proposed method outperforms current state-of-the-art by a large margin, achieving rank- 1/mAP accuracy 51.64%/50.11% on the SYSU-MM01 dataset, and 72.37%/69.09% on the RegDB dataset.Correlation filter (CF) is a critical technique to improve accuracy and speed in the field of visual object tracking. find more Despite being studied extensively, most existing CF methods suffer from failing to make the most of the inherent spatial-temporal prior of videos. To address this limitation, as consecutive frames are eminently resemble in most videos, we investigate a novel scheme to predict targets' future state by exploiting previous observations. Specifically, in this paper, we propose a prediction based CF tracking framework by learning the spatial-temporal similarity of consecutive frames for sample managing, template regularization, and training response pre-weighting. We model the learning problem theoretically as a novel objective and provide effective optimization algorithms to solve the learning task. In addition, we implement two CF trackers with different features. Extensive experiments are conducted on three popular benchmarks to validate our scheme. The encouraging results demonstrate that the proposed scheme can significantly boost the accuracy of CF tracking, and the two trackers achieve competitive performances against state-of-the-art trackers. We finally present a comprehensive analysis on the efficacy of our proposed method and the efficiency of our trackers to facilitate real-world visual tracking applications.Focused ultrasound (FUS)-based viscoelastic imaging techniques using high frame rate (HFR) ultrasound to track tissue displacement can be used for mechanistic monitoring of FUS neuromodulation. However, a majority of techniques avoid imaging during the active push transmit (interleaved or postpush acquisitions) to mitigate ultrasound interference, which leads to missing temporal information of ultrasound effects when FUS is being applied. Furthermore, critical for clinical translation, use