Hierarchical Masked 3D Diffusion Model for Video Outpainting

Video outpainting aims to adequately complete missing areas at the edges of video frames. Compared to image outpainting, it presents an additional challenge as the model should maintain the temporal consistency of the filled area. In this paper, we introduce a masked 3D diffusion model for video outpainting. We use the technique of mask modeling to train the 3D diffusion model. This allows us to use multiple guide frames to connect the results of multiple video clip inferences, thus ensuring temporal consistency and reducing jitter between adjacent frames. Meanwhile, we extract the global frames of the video as prompts and guide the model to obtain information other than the current video clip using cross-attention. We also introduce a hybrid coarse-to-fine inference pipeline to alleviate the artifact accumulation problem. The existing coarse-to-fine pipeline only uses the infilling strategy, which brings degradation because the time interval of the sparse frames is too large. Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames. Experiments show that our method achieves state-of-the-art results in video outpainting tasks. More results are provided at our https://fanfanda.github.io/M3DDM/.Comment: ACM MM 2023 accepte

Segmentation and Coverage Measurement of Maize Canopy Images for Variable-Rate Fertilization Using the MCAC-Unet Model

Excessive fertilizer use has led to environmental pollution and reduced crop yields, underscoring the importance of research into variable-rate fertilization (VRF) based on digital image technology in precision agriculture. Current methods, which rely on spectral sensors for monitoring and prescription mapping, face significant technical challenges, high costs, and operational complexities, limiting their widespread adoption. This study presents an automated, intelligent, and precise approach to maize canopy image segmentation using the multi-scale attention and Unet model to enhance VRF decision making, reduce fertilization costs, and improve accuracy. A dataset of maize canopy images under various lighting and growth conditions was collected and subjected to data augmentation and normalization preprocessing. The MCAC-Unet model, built upon the MobilenetV3 backbone network and integrating the convolutional block attention module (CBAM), atrous spatial pyramid pooling (ASPP) multi-scale feature fusion, and content-aware reassembly of features (CARAFE) adaptive upsampling modules, achieved a mean intersection over union (mIOU) of 87.51% and a mean pixel accuracy (mPA) of 93.85% in maize canopy image segmentation. Coverage measurements at a height of 1.1 m indicated a relative error ranging from 3.12% to 6.82%, averaging 4.43%, with a determination coefficient of 0.911, meeting practical requirements. The proposed model and measurement system effectively address the challenges in maize canopy segmentation and coverage assessment, providing robust support for crop monitoring and VRF decision making in complex environments

Biomedical Potential of Ultrafine Ag Nanoparticles Coated on Poly (Gamma-Glutamic Acid) Hydrogel with Special Reference to Wound Healing

In wound care management, the prevention of wound infection and the retention of an appropriate level of moisture are two major challenges. Therefore, designing an excellent antibacterial hydrogel with a suitable water-adsorbing capacity is very important to improve the development of wound dressings. In this paper, a novel silver nanoparticles/poly (gamma-glutamic acid) (γ-PGA) composite dressing was prepared for biomedical applications. The promoted wound-healing ability of the hydrogels were systematically evaluated with the aim of attaining a novel and effective wound dressing. A diffusion study showed that hydrogels can continuously release antibacterial factors (Ag). Hydrogels contain a high percentage of water, providing an ideal moist environment for tissue regeneration, while also preventing contraction of the wound. Moreover, an in vivo, wound-healing model evaluation of artificial wounds in mice indicated that silver/γ-PGA hydrogels could significantly promote wound healing. Histological examination revealed that hydrogels can successfully help to reconstruct intact epidermis and collagen deposition during 14 days of impaired wound healing. Overall, this research could shed new light on the design of antibacterial silver/γ-PGA hydrogels with potential applications in wound dressing