Cross-Modality Person Re-Identification Method with Joint-Modality Generation and Feature Enhancement.

In order to minimize the disparity between visible and infrared modalities and enhance pedestrian feature representation, a cross-modality person re-identification method is proposed, which integrates modality generation and feature enhancement. Specifically, a lightweight network is used for dimension reduction and augmentation of visible images, and intermediate modalities are generated to bridge the gap between visible images and infrared images. The Convolutional Block Attention Module is embedded into the ResNet50 backbone network to selectively emphasize key features sequentially from both channel and spatial dimensions. Additionally, the Gradient Centralization algorithm is introduced into the Stochastic Gradient Descent optimizer to accelerate convergence speed and improve generalization capability of the network model. Experimental results on SYSU-MM01 and RegDB datasets demonstrate that our improved network model achieves significant performance gains, with an increase in Rank-1 accuracy of 7.12% and 6.34%, as well as an improvement in mAP of 4.00% and 6.05%, respectively.

Enhancing Person Re-Identification through Attention-Driven Global Features and Angular Loss Optimization.

To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Specifically, this approach integrates a Normalization-based Channel Attention Module into the fundamental ResNet50 backbone, utilizing a scaling factor to prioritize and enhance key pedestrian feature information. Furthermore, dynamic activation functions are employed to adaptively modulate the parameters of ReLU based on the input convolutional feature maps, thereby bolstering the nonlinear expression capabilities of the network model. By incorporating Arcface loss into the cross-entropy loss, the supervised model is trained to learn pedestrian features that exhibit significant inter-class variance while maintaining tight intra-class coherence. The evaluation of the enhanced model on two popular datasets, Market1501 and DukeMTMC-ReID, reveals improvements in Rank-1 accuracy by 1.28% and 1.4%, respectively, along with corresponding gains in the mean average precision (mAP) of 1.93% and 1.84%. These findings indicate that the proposed model is capable of extracting more robust pedestrian features, enhancing feature discriminability, and ultimately achieving superior recognition accuracy.

CD-BFT: Canonical Decomposition-Based Belief Functions Transformation in Possibility Theory.

Based on subjective possibilistic semantics, an agent's subjective probability mass function is dominated by a qualitative Possibility Mass Function (PossMF), which can also be transformed into a unique consonant mass function. However, the existing transformation method cannot maintain the consistency of combination rules, i.e., fusing PossMFs and consonant mass functions with same information content, respectively, the results no longer maintain the reversible transformation. To address the above issue, a novel belief functions transformation is proposed, which can be interpreted based on both Smets' canonical decomposition and Pichon's canonical decomposition. The proposed method is validated based on consistency of combination rules, the least commitment principle, and its application in the fusion of information. In addition, based on the two canonical decompositions, we extend the transformation to possibilistic belief structure, and offer a new perspective of relationship between possibilistic information and evidential information.

Relationships between Diatom Abundances in Rat Organs and in Environmental Waters.

OBJECTIVE: A diagnosis of drowning remains one of the most challenging issues in forensic science, especially for decomposed bodies. Diatom analysis is considered as an encouraging method for diagnosing drowning. In this study, we developed a drowned rat model using different diatom densities in water. METHODS: A total of 120 adult Sprague-Dawley rats were used and divided into six groups, wherein experimental groups 1-5 were drowned rats (group A) and postmortem submersion rats (group B) that were submerged in water with five different Cyclotella sp. diatom densities, while the remaining group was used as a blank control. The combination of microwave digestion and vacuum filtration method was used to accomplish efficient tissue digestion and ascertain higher accuracy of diatom determinations within organs. RESULTS: The abundances of diatoms in the lungs, livers, and kidneys were significantly different. The diatom abundances in the lungs, livers, and kidneys were directly proportional to the water diatom densities, and specific quantitative relationships could be approximated by separate regression equations for each organ type. However, the trends associated with the diatom increases among organs slightly differed. In addition, the diatom abundances in the lungs, livers, and kidneys were all positively correlated. Diatoms were not observed in the postmortem submersion groups nor in the blank control groups. CONCLUSION: The results of this study provide valuable information for establishing a quantitative diatom framework for informing future forensic medicine efforts.