ViP-Mixer: A Convolutional Mixer for Video Prediction

Video prediction aims to predict future frames from a video's previous content. Existing methods mainly process video data where the time dimension mingles with the space and channel dimensions from three distinct angles: as a sequence of individual frames, as a 3D volume in spatiotemporal coordinates, or as a stacked image where frames are treated as separate channels. Most of them generally focus on one of these perspectives and may fail to fully exploit the relationships across different dimensions. To address this issue, this paper introduces a convolutional mixer for video prediction, termed ViP-Mixer, to model the spatiotemporal evolution in the latent space of an autoencoder. The ViP-Mixers are stacked sequentially and interleave feature mixing at three levels: frames, channels, and locations. Extensive experiments demonstrate that our proposed method achieves new state-of-the-art prediction performance on three benchmark video datasets covering both synthetic and real-world scenarios.Comment: Under revie

Towards Real-World Visual Tracking with Temporal Contexts

Visual tracking has made significant improvements in the past few decades. Most existing state-of-the-art trackers 1) merely aim for performance in ideal conditions while overlooking the real-world conditions; 2) adopt the tracking-by-detection paradigm, neglecting rich temporal contexts; 3) only integrate the temporal information into the template, where temporal contexts among consecutive frames are far from being fully utilized. To handle those problems, we propose a two-level framework (TCTrack) that can exploit temporal contexts efficiently. Based on it, we propose a stronger version for real-world visual tracking, i.e., TCTrack++. It boils down to two levels: features and similarity maps. Specifically, for feature extraction, we propose an attention-based temporally adaptive convolution to enhance the spatial features using temporal information, which is achieved by dynamically calibrating the convolution weights. For similarity map refinement, we introduce an adaptive temporal transformer to encode the temporal knowledge efficiently and decode it for the accurate refinement of the similarity map. To further improve the performance, we additionally introduce a curriculum learning strategy. Also, we adopt online evaluation to measure performance in real-world conditions. Exhaustive experiments on 8 wellknown benchmarks demonstrate the superiority of TCTrack++. Real-world tests directly verify that TCTrack++ can be readily used in real-world applications.Comment: Accepted by IEEE TPAMI, Code: https://github.com/vision4robotics/TCTrac

Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis

Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of intelligent transportation systems because of its versatility and effectiveness. As an emerging force in the revolutionary trend of deep learning, Siamese networks shine in UAV-based object tracking with their promising balance of accuracy, robustness, and speed. Thanks to the development of embedded processors and the gradual optimization of deep neural networks, Siamese trackers receive extensive research and realize preliminary combinations with UAVs. However, due to the UAV's limited onboard computational resources and the complex real-world circumstances, aerial tracking with Siamese networks still faces severe obstacles in many aspects. To further explore the deployment of Siamese networks in UAV-based tracking, this work presents a comprehensive review of leading-edge Siamese trackers, along with an exhaustive UAV-specific analysis based on the evaluation using a typical UAV onboard processor. Then, the onboard tests are conducted to validate the feasibility and efficacy of representative Siamese trackers in real-world UAV deployment. Furthermore, to better promote the development of the tracking community, this work analyzes the limitations of existing Siamese trackers and conducts additional experiments represented by low-illumination evaluations. In the end, prospects for the development of Siamese tracking for UAV-based intelligent transportation systems are deeply discussed. The unified framework of leading-edge Siamese trackers, i.e., code library, and the results of their experimental evaluations are available at https://github.com/vision4robotics/SiameseTracking4UAV

Apple Quality Evaluation Based on Entropy Weight Method, Grey Relational Degree Method and Low-field Nuclear Magnetic Resonance Detection

To study the quality characteristics of different apple varieties and establish a comprehensive evaluation model of apple quality, taking five varieties of apples (Tianshui Huaniu, Aksu Tangxin, Marshal Huang, Cream Fuji, and Luochuan Red Fuji) as the research object, the four texture characteristics, including hardness, adhesion, chewability, cohesion, and four physical and chemical indicators, including water content, titratable acid (TA), soluble sugar (SS), and soluble solid content (SSC) were tested. Combining the low-field nuclear magnetic resonance detection technology, the correlation between the water distribution and the physicochemical and texture characteristics of apple was explored, and the main indicators for evaluating apple quality were established by principal component analysis. Based on the entropy weight method, each core index was given weight, and a grey correlation degree evaluation model was established. The results showed that there were significant differences in various indexes of different varieties of apples (P<0.05), and there was a high correlation between their water distribution and texture characteristics and physical and chemical indexes. The spin-spin relaxation time T22 (immobilized water), T21 (bound water) and TA, SS, SSC were established as the core indexes. The weight calculated by entropy weight method showed that the sum of T22 and T21 was 35.31%, accounting for the largest proportion, indicating that the water distribution had the greatest impact on apple quality. The grey correlation analysis showed that the quality of Tianshui Huaniu and Aksu Tangxin was better. The method adopted in this study could quickly and accurately establish the quality evaluation model of apples, and provide a new method for the quality evaluation of fruits and vegetables including apples

What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery

Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in a safe and efficient manner. Yet, robot simulators inevitably exhibit disparities from the real world, yielding inaccuracies that manifest as the simulation-to-real gap. Existing literature has proposed to close this gap by actively modifying specific simulator parameters to align the simulated data with real-world observations. However, the set of tunable parameters is usually manually selected to reduce the search space in a case-by-case manner, which is hard to scale up for complex systems and requires extensive domain knowledge. To address the scalability issue and automate the parameter-tuning process, we introduce an approach that aligns the simulator with the real world by discovering the causal relationship between the environment parameters and the sim-to-real gap. Concretely, our method learns a differentiable mapping from the environment parameters to the differences between simulated and real-world robot-object trajectories. This mapping is governed by a simultaneously-learned causal graph to help prune the search space of parameters, provide better interpretability, and improve generalization. We perform experiments to achieve both sim-to-sim and sim-to-real transfer, and show that our method has significant improvements in trajectory alignment and task success rate over strong baselines in a challenging manipulation task

Evaluate the effects of low-intensity pulsed ultrasound on dental implant osseointegration under type II diabetes

Objective: The objective of this study is to assess the impact of low-intensity pulsed ultrasound (LIPUS) therapy on the peri-implant osteogenesis in a Type II diabetes mellitus (T2DM) rat model.Methods: A total of twenty male Sprague-Dawley (SD) rats were randomly allocated into four groups: Control group, T2DM group, Control-LIPUS group, and T2DM-LIPUS group. Implants were placed at the rats’ bilateral maxillary first molar sites. The LIPUS treatment was carried out on the rats in Control-LIPUS group and T2DM-LIPUS group, immediately after the placement of the implants, over three consecutive weeks. Three weeks after implantation, the rats’ maxillae were extracted for micro-CT, removal torque value (RTV), and histologic analysis.Results: Micro-CT analysis showed that T2DM rats experienced more bone loss around implant cervical margins compared with the non-T2DM rats, while the LIPUS treated T2DM rats showed similar bone heights to the non-T2DM rats. Bone-implant contact ratio (BIC) were lower in T2DM rats but significantly improved in the LIPUS treated T2DM rats. Bone formation parameters including bone volume fraction (BV/TV), trabecular thickness (Tb.Th), bone mineral density (BMD) and RTV were all positively influenced by LIPUS treatment. Histological staining further confirmed LIPUS’s positive effects on peri-implant new bone formation in T2DM rats.Conclusion: As an effective and safe treatment in promoting osteogenesis, LIPUS has a great potential for T2DM patients to attain improved peri-implant osteogenesis. To confirm its clinical efficacy and to explore the underlying mechanism, further prospective cohort studies or randomized controlled trials are needed in the future