Towards Grouping in Large Scenes with Occlusion-aware Spatio-temporal Transformers

Group detection, especially for large-scale scenes, has many potential applications for public safety and smart cities. Existing methods fail to cope with frequent occlusions in large-scale scenes with multiple people, and are difficult to effectively utilize spatio-temporal information. In this paper, we propose an end-to-end framework,GroupTransformer, for group detection in large-scale scenes. To deal with the frequent occlusions caused by multiple people, we design an occlusion encoder to detect and suppress severely occluded person crops. To explore the potential spatio-temporal relationship, we propose spatio-temporal transformers to simultaneously extract trajectory information and fuse inter-person features in a hierarchical manner. Experimental results on both large-scale and small-scale scenes demonstrate that our method achieves better performance compared with state-of-the-art methods. On large-scale scenes, our method significantly boosts the performance in terms of precision and F1 score by more than 10%. On small-scale scenes, our method still improves the performance of F1 score by more than 5%. The project page with code can be found at http://cic.tju.edu.cn/faculty/likun/projects/GroupTrans.Comment: 11 pages, 5 figure

Integration of ideological education into professional courses for international students in China

In this paper, we provide the formal definition about the so called “Ideological Curriculum (IC)” in the universities of China, namely, integrating ideological education in the professional courses and curriculum. The significance, current trend and situation of IC are introduced. The different of ideological education between international and local students are analysed. With a specific case, we further introduce how to integrate the ideological education into a professional course for international students in China

CCM: Adding Conditional Controls to Text-to-Image Consistency Models

Consistency Models (CMs) have showed a promise in creating visual content efficiently and with high quality. However, the way to add new conditional controls to the pretrained CMs has not been explored. In this technical report, we consider alternative strategies for adding ControlNet-like conditional control to CMs and present three significant findings. 1) ControlNet trained for diffusion models (DMs) can be directly applied to CMs for high-level semantic controls but struggles with low-level detail and realism control. 2) CMs serve as an independent class of generative models, based on which ControlNet can be trained from scratch using Consistency Training proposed by Song et al. 3) A lightweight adapter can be jointly optimized under multiple conditions through Consistency Training, allowing for the swift transfer of DMs-based ControlNet to CMs. We study these three solutions across various conditional controls, including edge, depth, human pose, low-resolution image and masked image with text-to-image latent consistency models.Comment: Project Page: https://swiftforce.github.io/CC

Towards grouping in large scenes with occlusion-aware spatio-temporal transformers

Group detection, especially for large-scale scenes, has many potential applications for public safety and smart cities. Existing methods fail to cope with frequent occlusions in large-scale scenes with multiple people, and are difficult to effectively utilize spatio-temporal information. In this paper, we propose an end-to-end framework, GroupTransformer , for group detection in large-scale scenes. To deal with the frequent occlusions caused by multiple people, we design an occlusion encoder to detect and suppress severely occluded person crops. To explore the potential spatio-temporal relationship, we propose spatio-temporal transformers to simultaneously extract trajectory information and fuse inter-person features in a hierarchical manner. Experimental results on both large-scale and small-scale scenes demonstrate that our method achieves better performance compared with state-of-the-art methods. On large-scale scenes, our method significantly boosts the performance in terms of precision and F1 score by more than 10%. On small-scale scenes, our method still improves the performance of F1 score by more than 5%. We will release the code for research purposes

A review on electrospun magnetic nanomaterials:methods, properties and applications

Magnetic materials display attractive properties for a wide range of applications. More recently, interest has turned to significantly enhancing their behaviour for advanced technologies, by exploiting the remarkable advantages that nanoscale materials offer over their bulk counterparts. Electrospinning is a high-throughput method that can continuously produce nanoscale fibres, providing a versatile way to prepare novel magnetic nanomaterials. This article reviews 20 years of magnetic nanomaterials fabricated via electrospinning and introduces their two primary production methods: electrospinning polymer-based magnetic fibres directly from solution and electrospinning fibrous templates for post-treatment. Continual advances in electrospinning have enabled access to a variety of morphologies, which has led to magnetic materials having desirable flexibility, anisotropy and high specific surface area. Post-treatment methods, such as surface deposition, carbonization and calcination, further improve or even create unique magnetic properties in the materials. This renders them useful in broad ranging applications, including electromagnetic interference shielding (EMS), magnetic separation, tissue engineering scaffolding, hyperthermia treatment, drug delivery, nanogenerators and data storage. The processing methods of electrospun magnetic nanofibres, their properties and related applications are discussed throughout this review. Key areas for future research have been highlighted with the aim of stimulating advances in the development of electrospun magnetic nanomaterials for a wide range of applications

Engineering hibiscus-like riboflavin/ZIF-8 microsphere composites to enhance transepithelial corneal cross-linking

Riboflavin-5-phosphate (RF) is the most commonly used photosensitizer in corneal cross-linking (CXL), but its hydrophilicity and negative charge limit its penetration through the corneal epithelium into the stroma. To enhance the corneal permeability of RF and promote its efficacy in the treatment of keratoconus, novel hibiscus-like RF@ZIF-8 microsphere composites [6RF@ZIF-8 NF (nanoflake)] are prepared using ZIF-8 nanomaterials as carriers, which are characterized by their hydrophobicity, positive potential, biocompatibility, high loading capacities, and large surface areas. Both hematoxylin and eosin endothelial staining and TUNEL assays demonstrate excellent biocompatibility of 6RF@ZIF-8 NF. In in vivo studies, the 6RF@ZIF-8 NF displayed excellent corneal permeation, and outstanding transepithelial CXL (TE-CXL) efficacy, slightly better than the conventional CXL protocol. Furthermore, the special hibiscus-like structures of 6RF@ZIF-8 NF meant that it has better TE-CXL efficacy than that of 6RF@ZIF-8 NP (nanoparticles) due to the larger contact area with the epithelium and the shorter RF release passage. These results suggest that the 6RF@ZIF-8 NF are promising for transepithelial corneal cross-linking, avoiding the need for epithelial debridement

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies