Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification

Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance. Existing methods mainly focus on learning a shared feature space to reduce the modality discrepancy between visible and infrared modalities, which still leave two problems underexplored: information redundancy and modality complementarity. To this end, properly eliminating the identity-irrelevant information as well as making up for the modality-specific information are critical and remains a challenging endeavor. To tackle the above problems, we present a novel mutual information and modality consensus network, namely CMInfoNet, to extract modality-invariant identity features with the most representative information and reduce the redundancies. The key insight of our method is to find an optimal representation to capture more identity-relevant information and compress the irrelevant parts by optimizing a mutual information bottleneck trade-off. Besides, we propose an automatically search strategy to find the most prominent parts that identify the pedestrians. To eliminate the cross- and intra-modality variations, we also devise a modality consensus module to align the visible and infrared modalities for task-specific guidance. Moreover, the global-local feature representations can also be acquired for key parts discrimination. Experimental results on four benchmarks, i.e., SYSU-MM01, RegDB, Occluded-DukeMTMC, Occluded-REID, Partial-REID and Partial\_iLIDS dataset, have demonstrated the effectiveness of CMInfoNet

ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data

Generative Adversarial Networks (GANs) typically suffer from overfitting when limited training data is available. To facilitate GAN training, current methods propose to use data-specific augmentation techniques. Despite the effectiveness, it is difficult for these methods to scale to practical applications. In this work, we present ScoreMix, a novel and scalable data augmentation approach for various image synthesis tasks. We first produce augmented samples using the convex combinations of the real samples. Then, we optimize the augmented samples by minimizing the norms of the data scores, i.e., the gradients of the log-density functions. This procedure enforces the augmented samples close to the data manifold. To estimate the scores, we train a deep estimation network with multi-scale score matching. For different image synthesis tasks, we train the score estimation network using different data. We do not require the tuning of the hyperparameters or modifications to the network architecture. The ScoreMix method effectively increases the diversity of data and reduces the overfitting problem. Moreover, it can be easily incorporated into existing GAN models with minor modifications. Experimental results on numerous tasks demonstrate that GAN models equipped with the ScoreMix method achieve significant improvements

Commercial Value Assessment of “Grey Space” under Overpasses: Analytic Hierarchy Process

Although the rise of urban overpasses has optimized the urban transport system and improved the spatial structure of the city, the use of space under overpasses has many problems, and they can be dark, short, unpleasant, and abandoned spaces which are full of girders and include ill-shaped areas in some places. This study aims at the recent study of space utilization under overpasses. Taking the Xudong district in Wuhan as an example, the multistandards weight analysis was conducted to evaluate the value of the commercial form of the “grey space” under overpasses and analyzed the feasibility of commercial forms

Heterogeneous Face Recognition via Face Synthesis with Identity-Attribute Disentanglement

Heterogeneous Face Recognition (HFR) aims to match faces across different domains (e.g., visible to near-infrared images), which has been widely applied in authentication and forensics scenarios. However, HFR is a challenging problem because of the large cross-domain discrepancy, limited heterogeneous data pairs, and large variation of facial attributes. To address these challenges, we propose a new HFR method from the perspective of heterogeneous data augmentation, named Face Synthesis with Identity-Attribute Disentanglement (FSIAD). Firstly, the identity-attribute disentanglement (IAD) decouples face images into identity-related representations and identity-unrelated representations (called attributes), and then decreases the correlation between identities and attributes. Secondly, we devise a face synthesis module (FSM) to generate a large number of images with stochastic combinations of disentangled identities and attributes for enriching the attribute diversity of synthetic images. Both the original images and the synthetic ones are utilized to train the HFR network for tackling the challenges and improving the performance of HFR. Extensive experiments on five HFR databases validate that FSIAD obtains superior performance than previous HFR approaches. Particularly, FSIAD obtains 4.8% improvement over state of the art in terms of VR@FAR=0.01% on LAMP-HQ, the largest HFR database so far.Comment: Accepted for publication in IEEE Transactions on Information Forensics and Security (TIFS

Mechanochemical synthesis of nanostructured Sr(Ti1–xFex)O3–δ solid-solution powders and their surface photovoltage responses

A series of nanostructure Sr(Ti1−xFex)O3−δ (STFx, x=0.4, 0.6, 0.8) solid-solution powders were synthesized by mechanochemical approach milling from the mixture of SrO, Fe2O3 and TiO2 metal oxides at room temperature. The XRD results revealed that the perovskite STFx nanoparticles were finally formed with few residual α-Fe2O3 detected dependent on the milling conditions. The structure evolution suggested that the mechanochemical synthesis underwent via a solid-state reaction route to initially form Ti-rich perovskite and then incorporate with the residual α-Fe2O3 to achieve the estimated composition. The synthesized STF08 powders exhibited the significant Surface Photovoltage (SPV) spectrum response both in UV and in visible-light region with p-type semiconductor behavior. This finding suggested that the synthesized STF nanopowders could potentially utilize more solar spectrum energy effectively for photo-oxidation and photo-catalysis applications

Modulation of host HIF-1α activity and the tryptophan pathway contributes to the anti-Toxoplasma gondii potential of nanoparticles

Background: Toxoplasmosis constitutes a large global burden that is further exacerbated by the shortcomings of available therapeutic options, thus underscoring the urgent need for better anti-Toxoplasma gondii therapy or strategies. Recently, we showed that the anti-parasitic action of inorganic nanoparticles (NPs) could, in part, be due to changes in redox status as well as in the parasite mitochondrial membrane potential. Methods: In the present study, we explored the in vitro mode of action of the anti-T. gondii effect of NPs by evaluating the contributions of host cellular processes, including the tryptophan pathway and hypoxia-inducing factor activity. NPs, at concentrations ranging from 0.01 to 200 µg/ml were screened for anti-parasitic activity. Sulfadiazine and/or pyrimethamine served as positive controls. Results: We found that interplay among multiple host cellular processes, including HIF-1α activity, indoleamine 2,3-dioxygenase activity, and to a larger extent the tryptophan pathway, contribute to the anti-parasitic action of NPs. Conclusion: To our knowledge, this is the first study to demonstrate an effect of NPs on the tryptophan and/or kynurenine pathway. General significance: Our findings deepen our understanding of the mechanism of action of NPs and suggest that modulation of the host nutrient pool may represent a viable approach to the development of new and effective anti-parasitic agents