whu-nercms at trecvid2021:instance search task

We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper. This year we participate in the automatic and interactive tasks of Instance Search (INS). For the automatic task, the retrieval target is divided into two parts, person retrieval, and action retrieval. We adopt a two-stage method including face detection and face recognition for person retrieval and two kinds of action detection methods consisting of three frame-based human-object interaction detection methods and two video-based general action detection methods for action retrieval. After that, the person retrieval results and action retrieval results are fused to initialize the result ranking lists. In addition, we make attempts to use complementary methods to further improve search performance. For interactive tasks, we test two different interaction strategies on the fusion results. We submit 4 runs for automatic and interactive tasks respectively. The introduction of each run is shown in Table 1. The official evaluations show that the proposed strategies rank 1st in both automatic and interactive tracks.Comment: 9 pages, 4 figure

Smithian platform-bearing gondolellid conodonts from Yiwagou Section, northwestern China and implications for their geographic distribution in the Early Triassic

Abundant platform-bearing gondolellid conodonts, including Scythogondolella mosheri (Kozur and Mostler), Sc. phryna Orchard and Zonneveld, and Sc. cf. milleri (Müller), have been discovered from the Yiwagou Section of Tewo, together with Novispathodus waageni waageni (Sweet) and Nv. w. eowaageni Zhao and Orchard. This is the first report of Smithian platform-bearing gondolellids from the Paleo-Tethys region. In addition, Eurygnathodus costatus Staesche, E. hamadai(Koike), Parafurnishius xuanhanensis Yang et al., and the genera Pachycladina Staesche, Parachirognathus Clark, and Hadrodontina Staesche have also been recovered from Dienerian to Smithian strata at Yiwagou Section. Three conodont zones are established, in ascending order: Eurygnathodus costatus-E. hamadai Assemblage Zone, Novispathodus waageni-Scythogondolella mosheri Assemblage Zone, and the Pachycladina-Parachirognathus Assemblage Zone. The platform-bearing gondolellids were globally distributed just after the end-Permian mass extinction, but the formerly abundant Clarkina Kozur disappeared in the late Griesbachian. Platform-bearing gondolellids dramatically decreased to a minimum of diversity and extent in the Dienerian before recovering in the Smithian. Scythogondolella Kozur, probably a thermophilic and eurythermic genus, lived in all latitudes at this time whereas other genera did not cope with Smithian high temperatures and so became restricted to the high-latitude regions. However, the maximum temperature in the late Smithian likely caused the extinction of almost all platform-bearing gondolellids. Finally, the group returned to equatorial regions and achieved global distribution again in the cooler conditions of the late Spathian. We conclude that temperature (and to a lesser extent oxygen levels) exerted a strong control on the geographical distribution and evolution of platform-bearing gondolellids in the Early Triassic

Deepfake Face Traceability with Disentangling Reversing Network

Deepfake face not only violates the privacy of personal identity, but also confuses the public and causes huge social harm. The current deepfake detection only stays at the level of distinguishing true and false, and cannot trace the original genuine face corresponding to the fake face, that is, it does not have the ability to trace the source of evidence. The deepfake countermeasure technology for judicial forensics urgently calls for deepfake traceability. This paper pioneers an interesting question about face deepfake, active forensics that "know it and how it happened". Given that deepfake faces do not completely discard the features of original faces, especially facial expressions and poses, we argue that original faces can be approximately speculated from their deepfake counterparts. Correspondingly, we design a disentangling reversing network that decouples latent space features of deepfake faces under the supervision of fake-original face pair samples to infer original faces in reverse.Comment: 5 pages, 4 figure

Part-Aware Refinement Network for Occlusion Vehicle Detection

Traditional machine learning approaches are susceptible to factors such as object scale, occlusion, leading to low detection efficiency and poor versatility in vehicle detection applications. To tackle this issue, we propose a part-aware refinement network, which combines multi-scale training and component confidence generation strategies in vehicle detection. Specifically, we divide the original single-valued prediction confidence and adopt the confidence of the visible part of the vehicle to correct the absolute detection confidence of the vehicle. That reduces the impact of occlusion on the detection effect. Simultaneously, we relabel the KITTI data, adding the detailed occlusion information of the vehicles. Then, the deep neural network model is trained and tested using the new images. Our proposed method can automatically extract the vehicle features and solve larger error problems when locating vehicles in traditional approaches. Extensive experimental results on KITTI datasets show that our method significantly outperforms the state-of-the-arts while maintaining the detection time

Part-Aware Refinement Network for Occlusion Vehicle Detection

Traditional machine learning approaches are susceptible to factors such as object scale, occlusion, leading to low detection efficiency and poor versatility in vehicle detection applications. To tackle this issue, we propose a part-aware refinement network, which combines multi-scale training and component confidence generation strategies in vehicle detection. Specifically, we divide the original single-valued prediction confidence and adopt the confidence of the visible part of the vehicle to correct the absolute detection confidence of the vehicle. That reduces the impact of occlusion on the detection effect. Simultaneously, we relabel the KITTI data, adding the detailed occlusion information of the vehicles. Then, the deep neural network model is trained and tested using the new images. Our proposed method can automatically extract the vehicle features and solve larger error problems when locating vehicles in traditional approaches. Extensive experimental results on KITTI datasets show that our method significantly outperforms the state-of-the-arts while maintaining the detection time

An Improved Frequency-Adaptive Virtual Variable Sampling-Based Repetitive Control for an Active Power Filter

To eliminate the harmonics caused by nonlinear loads, repetitive controllers are widely applied as current controllers for active power filters (APF). In practice, a variation in grid frequency leads to the appearance of a fractional-order delay filter. As a result, the resonant frequency of the repetitive controller will deviate from the fundamental frequency and the controller cannot compensate for harmonics accurately. To solve this problem, an improved frequency-adaptive repetitive controller based on virtual variable sampling (IMFA-VVS-RC) for APF is proposed in this paper. To enhance the system stability margin, the proposed RC introduces an infinite impulse response (IIR) low-pass filter. The proposed RC has a high stability margin at high frequencies due to the low gain of the IIR low-pass filter in the region above the cutoff frequency. In this way, the influence of model uncertainty and parameter uncertainty on system stability are reduced at high frequencies. At the same time, compared with the conventional repetitive controller (CRC), the proposed RC for APF has a better harmonic suppression ability when the frequency varies. Experiments have verified the effectiveness of the scheme adopted for APF