Graph based Label Enhancement for Multi-instance Multi-label learning

Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously. The related labels in existing MIML are all assumed as logical labels with equal significance. However, in practical applications in MIML, significance of each label for multiple instances per bag (such as an image) is significant different. Ignoring labeling significance will greatly lose the semantic information of the object, so that MIML is not applicable in complex scenes with a poor learning performance. To this end, this paper proposed a novel MIML framework based on graph label enhancement, namely GLEMIML, to improve the classification performance of MIML by leveraging label significance. GLEMIML first recognizes the correlations among instances by establishing the graph and then migrates the implicit information mined from the feature space to the label space via nonlinear mapping, thus recovering the label significance. Finally, GLEMIML is trained on the enhanced data through matching and interaction mechanisms. GLEMIML (AvgRank: 1.44) can effectively improve the performance of MIML by mining the label distribution mechanism and show better results than the SOTA method (AvgRank: 2.92) on multiple benchmark datasets.Comment: 7 pages,2 figure

Multi-View Stereo Network Based on Attention Mechanism and Neural Volume Rendering

Due to the presence of regions with weak textures or non-Lambertian surfaces, feature matching in learning-based Multi-View Stereo (MVS) algorithms often leads to incorrect matches, resulting in the construction of the flawed cost volume and incomplete scene reconstruction. In response to this limitation, this paper introduces the MVS network based on attention mechanism and neural volume rendering. Firstly, we employ a multi-scale feature extraction module based on dilated convolution and attention mechanism. This module enables the network to accurately model inter-pixel dependencies, focusing on crucial information for robust feature matching. Secondly, to mitigate the impact of the flawed cost volume, we establish a neural volume rendering network based on multi-view semantic features and neural encoding volume. By introducing the rendering reference view loss, we infer 3D geometric scenes, enabling the network to learn scene geometry information beyond the cost volume representation. Additionally, we apply the depth consistency loss to maintain geometric consistency across networks. The experimental results indicate that on the DTU dataset, compared to the CasMVSNet method, the completeness of reconstructions improved by 23.1%, and the Overall increased by 7.3%. On the intermediate subset of the Tanks and Temples dataset, the average F-score for reconstructions is 58.00, which outperforms other networks, demonstrating superior reconstruction performance and strong generalization capability

LSO-FastSLAM: A New Algorithm to Improve the Accuracy of Localization and Mapping for Rescue Robots

This paper improves the accuracy of a mine robot’s positioning and mapping for rapid rescue. Specifically, we improved the FastSLAM algorithm inspired by the lion swarm optimization method. Through the division of labor between different individuals in the lion swarm optimization algorithm, the optimized particle set distribution after importance sampling in the FastSLAM algorithm is realized. The particles are distributed in a high likelihood area, thereby solving the problem of particle weight degradation. Meanwhile, the diversity of particles is increased since the foraging methods between individuals in the lion swarm algorithm are different so that improving the accuracy of the robot’s positioning and mapping. The experimental results confirmed the improvement of the algorithm and the accuracy of the robot

Quantum Dot Nanobeads Based Fluorescence Immunoassay for the Quantitative Detection of Sulfamethazine in Chicken and Milk

Sulfamethazine (SMZ) as a broad antibiotic is widely used in livestock and poultry. However, the abuse of SMZ in livestock feed can lead to SMZ residues in food and the resistance of bacteria to drugs. Thus, a method for the detection of SMZ in food is urgently needed. In this study, quantum dot (QD) nanobeads (QBs) were synthesized by encapsulating CdSe/ZnS QDs using a microemulsion technique. The prepared QBs as signal probes were applied in lateral flow immunoassay (LFIA) for the detection of SMZ in chicken and milk. Our proposed method had limits of detection of 0.1138–0.0955 ng/mL and corresponding linear ranges of 0.2–12.5, 0.1–15 ng/mL in chicken and milk samples, respectively. The recovery of LFIA for the detection of SMZ was 80.9–109.4% and 84–101.6% in chicken and milk samples, respectively. Overall, the developed QBs-LFIA had high reliability and excellent potential for rapid and sensitive screening of SMZ in food

Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours

With the rapid development of computer vision, the application of computer vision to precision farming in animal husbandry is currently a hot research topic. Due to the scale of goose breeding continuing to expand, there are higher requirements for the efficiency of goose farming. To achieve precision animal husbandry and to avoid human influence on breeding, real-time automated monitoring methods have been used in this area. To be specific, on the basis of instance segmentation, the activities of individual geese are accurately detected, counted, and analyzed, which is effective for achieving traceability of the condition of the flock and reducing breeding costs. We trained QueryPNet, an advanced model, which could effectively perform segmentation and extraction of geese flock. Meanwhile, we proposed a novel neck module that improved the feature pyramid structure, making feature fusion more effective for both target detection and instance individual segmentation. At the same time, the number of model parameters was reduced by a rational design. This solution was tested on 639 datasets collected and labeled on specially created free-range goose farms. With the occlusion of vegetation and litters, the accuracies of the target detection and instance segmentation reached 0.963 ([email protected]) and 0.963 ([email protected]), respectively