124 research outputs found
Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement
Unsupervised learning visible-infrared person re-identification (USL-VI-ReID)
aims at learning modality-invariant features from unlabeled cross-modality
dataset, which is crucial for practical applications in video surveillance
systems. The key to essentially address the USL-VI-ReID task is to solve the
cross-modality data association problem for further heterogeneous joint
learning. To address this issue, we propose a Dual Optimal Transport Label
Assignment (DOTLA) framework to simultaneously assign the generated labels from
one modality to its counterpart modality. The proposed DOTLA mechanism
formulates a mutual reinforcement and efficient solution to cross-modality data
association, which could effectively reduce the side-effects of some
insufficient and noisy label associations. Besides, we further propose a
cross-modality neighbor consistency guided label refinement and regularization
module, to eliminate the negative effects brought by the inaccurate supervised
signals, under the assumption that the prediction or label distribution of each
example should be similar to its nearest neighbors. Extensive experimental
results on the public SYSU-MM01 and RegDB datasets demonstrate the
effectiveness of the proposed method, surpassing existing state-of-the-art
approach by a large margin of 7.76% mAP on average, which even surpasses some
supervised VI-ReID methods
Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID
Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to
match pedestrian images of the same identity from different modalities without
annotations. Existing works mainly focus on alleviating the modality gap by
aligning instance-level features of the unlabeled samples. However, the
relationships between cross-modality clusters are not well explored. To this
end, we propose a novel bilateral cluster matching-based learning framework to
reduce the modality gap by matching cross-modality clusters. Specifically, we
design a Many-to-many Bilateral Cross-Modality Cluster Matching (MBCCM)
algorithm through optimizing the maximum matching problem in a bipartite graph.
Then, the matched pairwise clusters utilize shared visible and infrared
pseudo-labels during the model training. Under such a supervisory signal, a
Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework
is proposed to align features jointly at a cluster-level. Meanwhile, the
cross-modality Consistency Constraint (CC) is proposed to explicitly reduce the
large modality discrepancy. Extensive experiments on the public SYSU-MM01 and
RegDB datasets demonstrate the effectiveness of the proposed method, surpassing
state-of-the-art approaches by a large margin of 8.76% mAP on average
Ultrafine-Grained Materials Fabrication with High Pressure Torsion and Simulation of Plastic Deformation Inhomogeneous Characteristics
Utilization of severe plastic deformation (SPD) methods has provided a convenient approach for producing ultrafine-grained (UFG) materials exhibiting outstanding characteristics especially mechanical properties. HPT as one of the SPD methods can lead both to smaller grains and to a higher fraction of high-angle grain boundaries, which is an especially attractive procedure by researchers. In order to understand the nonlinearities relationship between the mechanical properties and the developed strain during plastic deformation, local deformation analysis using the finite element methodwas applied for the HPT process. In this chapter, results are reported of an investigation on the deformed microstructure and mechanical properties of different materials samples during the HPT process using experiments and FEM simulations. Simulation results indicate that the disks show inhomogeneity development and distribution of strain and stress during the plastic deformation. Microstructure and hardness investigation results can give a well support to verify the rules of inhomogenous plastic deformation in the early stage of the HPT disks. Furthermore, the friction and anvil geometry play important roles in the homogeneity of the deformation. After the hollow cone high pressure torsion (HC-HPT), the thermal stability of Zr64.13Cu15.75Ni10.12Al10 BMGs is enhanced, while the elastic modulus of BMG will be decreased
DeftectNet: Joint loss structured deep adversarial network for thermography defect detecting system
In this paper, a novel joint loss Generative Adversarial Networks (GAN) framework is proposed for thermography nondestructive testing named Defect-Detection Network (DeftectNet). A new joint loss function that incorporates both the modified GAN loss and penalty loss is proposed. The strategy enables the training process to be more stable and to significantly improve the detection rate. The obtained result shows that the proposed joint loss can better capture the salient features in order to improve the detection accuracy. In order to verify the effectiveness and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer/plastic (CFRP) specimens. A comparison experiment has been undertaken to study the proposed method with other current state-of-the-art deep semantic segmentation algorithms. The promising results have been obtained where the performance of the proposed method can achieve end-to-end detection of defects
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