Mutual Distillation Learning For Person Re-Identification

With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements. However, the majority of prior works have traditionally focused on solving the problem via extracting features solely from a single perspective, such as uniform partitioning, hard attention mechanisms, or semantic masks. While these approaches have demonstrated efficacy within specific contexts, they fall short in diverse situations. In this paper, we propose a novel approach, Mutual Distillation Learning For Person Re-identification (termed as MDPR), which addresses the challenging problem from multiple perspectives within a single unified model, leveraging the power of mutual distillation to enhance the feature representations collectively. Specifically, our approach encompasses two branches: a hard content branch to extract local features via a uniform horizontal partitioning strategy and a Soft Content Branch to dynamically distinguish between foreground and background and facilitate the extraction of multi-granularity features via a carefully designed attention mechanism. To facilitate knowledge exchange between these two branches, a mutual distillation and fusion process is employed, promoting the capability of the outputs of each branch. Extensive experiments are conducted on widely used person ReID datasets to validate the effectiveness and superiority of our approach. Notably, our method achieves an impressive $88.7\%/94.4\%$ in mAP/Rank-1 on the DukeMTMC-reID dataset, surpassing the current state-of-the-art results. Our source code is available at https://github.com/KuilongCui/MDPR

VIoTGPT: Learning to Schedule Vision Tools towards Intelligent Video Internet of Things

Video Internet of Things (VIoT) has shown full potential in collecting an unprecedented volume of video data. Learning to schedule perceiving models and analyzing the collected videos intelligently will be potential sparks for VIoT. In this paper, to address the challenges posed by the fine-grained and interrelated vision tool usage of VIoT, we build VIoTGPT, the framework based on LLMs to correctly interact with humans, query knowledge videos, and invoke vision models to accomplish complicated tasks. To support VIoTGPT and related future works, we meticulously crafted the training dataset and established benchmarks involving 11 representative vision models across three categories based on semi-automatic annotations. To guide LLM to act as the intelligent agent towards intelligent VIoT, we resort to ReAct instruction tuning based on the collected VIoT dataset to learn the tool capability. Quantitative and qualitative experimental results and analyses demonstrate the effectiveness of VIoTGPT

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

The raw depth image captured by indoor depth sensors usually has an extensive range of missing depth values due to inherent limitations such as the inability to perceive transparent objects and the limited distance range. The incomplete depth map with missing values burdens many downstream vision tasks, and a rising number of depth completion methods have been proposed to alleviate this issue. While most existing methods can generate accurate dense depth maps from sparse and uniformly sampled depth maps, they are not suitable for complementing large contiguous regions of missing depth values, which is common and critical in images captured in indoor environments. To overcome these challenges, we design a novel two-branch end-to-end fusion network named RDFC-GAN, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map. The first branch employs an encoder-decoder structure, by adhering to the Manhattan world assumption and utilizing normal maps from RGB-D information as guidance, to regress the local dense depth values from the raw depth map. In the other branch, we propose an RGB-depth fusion CycleGAN to transfer the RGB image to the fine-grained textured depth map. We adopt adaptive fusion modules named W-AdaIN to propagate the features across the two branches, and we append a confidence fusion head to fuse the two outputs of the branches for the final depth map. Extensive experiments on NYU-Depth V2 and SUN RGB-D demonstrate that our proposed method clearly improves the depth completion performance, especially in a more realistic setting of indoor environments, with the help of our proposed pseudo depth maps in training.Comment: Haowen Wang and Zhengping Che are with equal contributions. Under review. An earlier version has been accepted by CVPR 2022 (arXiv:2203.10856

Phomopsis longanae Chi-Induced Change in ROS Metabolism and Its Relation to Pericarp Browning and Disease Development of Harvested Longan Fruit

Phomopsis longanae Chi is a major pathogenic fungus that infects harvested longan fruit. This study aimed to investigate the effects of P. longanae on reactive oxygen species (ROS) metabolism and its relation to the pericarp browning and disease development of harvested longan fruit during storage at 28°C and 90% relative humidity. Results showed that compared to the control longans, P. longanae-inoculated longans displayed higher indexes of pericarp browning and fruit disease, higher O2-. generation rate, higher accumulation of malondialdehyde (MDA), lower contents of glutathione (GSH) and ascorbic acid (AsA), lower 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging ability and reducing power in pericarp. In addition, P. longanae-infected longans exhibited higher activities of superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) in the first 2 days of storage, and lower activities of SOD, CAT, and APX during storage day 2–5 than those in the control longans. These findings indicated that pericarp browning and disease development of P. longanae-infected longan fruit might be the result of the reducing ROS scavenging ability and the increasing O2-. generation rate, which might lead to the peroxidation of membrane lipid, the loss of compartmentalization in longan pericarp cells, and subsequently cause polyphenol oxidase (PPO) and peroxidase (POD) to contact with phenolic substrates which result in enzymatic browning of longan pericarp, as well as cause the decrease of disease resistance to P. longanae and stimulate disease development of harvested longan fruit

Detecting fitness epistasis in recently admixed populations with genome-wide data

Background: Fitness epistasis, the interaction effect of genes at different loci on fitness, makes an important contribution to adaptive evolution. Although fitness interaction evidence has been observed in model organisms, it is more difficult to detect and remains poorly understood in human populations as a result of limited statistical power and experimental constraints. Fitness epistasis is inferred from non-independence between unlinked loci. We previously observed ancestral block correlation between chromosomes 4 and 6 in African Americans. The same approach fails when examining ancestral blocks on the same chromosome due to the strong confounding effect observed in a recently admixed population. Results: We developed a novel approach to eliminate the bias caused by admixture linkage disequilibrium when searching for fitness epistasis on the same chromosome. We applied this approach in 16,252 unrelated African Americans and identified significant ancestral correlations in two pairs of genomic regions (P-value\u3c 8.11 × 10- 7) on chromosomes 1 and 10. The ancestral correlations were not explained by population admixture. Historical African-European crossover events are reduced between pairs of epistatic regions. We observed multiple pairs of co-expressed genes shared by the two regions on each chromosome, including ADAR being co-expressed with IFI44 in almost all tissues and DARC being co-expressed with VCAM1, S1PR1 and ELTD1 in multiple tissues in the Genotype-Tissue Expression (GTEx) data. Moreover, the co-expressed gene pairs are associated with the same diseases/traits in the GWAS Catalog, such as white blood cell count, blood pressure, lung function, inflammatory bowel disease and educational attainment. Conclusions: Our analyses revealed two instances of fitness epistasis on chromosomes 1 and 10, and the findings suggest a potential approach to improving our understanding of adaptive evolution

Should Government Play a Strict or Lenient Role? An Evolutionary Game Analysis of Implementing the Forest Ecological Bank Policy

As one of the specific practices of natural resource index trading, the forest ecological bank policy (FEB) is essentially a market-based tool. With the deepening of ecological governance, the FEB policy has also become the main method chosen to solve the economic development problems in ecologically rich “low-lying” areas. However, in the process of implementing the FEB policy, the differences in the demands of various stakeholders were found to have led to a complex game phenomenon, resulting in deviations in policy implementation. This study constructs a multiplayer evolutionary game model between local governments and enterprises of different scales and analyzes the evolutionary stabilization strategy (ESS) in the implementation of the FEB policy. The results show that, under different conditions, there are three stabilization strategies in the evolutionary game system, these correspond to F1 (0, 0, 0), F4 (0, 1, 1), and F5 (1, 0, 0), respectively, the implications are that the strict government role with an active regulatory strategy leads to companies of different sizes refusing to participate (i.e., F5) and the lax government role with a negative regulatory strategy leads to companies of different sizes refusing to participate (i.e., F1) or choosing to participate (i.e., F4). Among them, the strict government role stimulates the companies to participate in the FEB policy through the high intensity of government regulation. In addition, as the policy continues to be implemented, the influence of the strict regulation on the “participation” behavior of the companies decreases. Conversely, the lax government role allows the companies to give full play to their autonomy and obtain higher ecological and environmental benefits

The transcriptional regulator JAZ8 interacts with the C2 protein from geminiviruses and limits the geminiviral infection in Arabidopsis

Jasmonates (JAs) are phytohormones that finely regulate critical biological processes, including plant development and defense. JASMONATE ZIM-DOMAIN (JAZ) proteins are crucial transcriptional regulators that keep JA-responsive genes in a repressed state. In the presence of JA-Ile, JAZ repressors are ubiquitinated and targeted for degradation by the ubiquitin/proteasome system, allowing the activation of downstream transcription factors and, consequently, the induction of JA-responsive genes. A growing body of evidence has shown that JA signalling is crucial in defending against plant viruses and their insect vectors. Here, we describe the interaction of C2 proteins from two tomato-infecting geminiviruses from the genus Begomovirus, tomato yellow leaf curl virus (TYLCV) and tomato yellow curl Sardinia virus (TYLCSaV), with the transcriptional repressor JAZ8 from Arabidopsis thaliana and its closest orthologue in tomato, SlJAZ9. Both JAZ and C2 proteins colocalize in the nucleus, forming discrete nuclear speckles. Overexpression of JAZ8 did not lead to altered responses to TYLCV infection in Arabidopsis; however, knock-down of JAZ8 favours geminiviral infection. Low levels of JAZ8 likely affect the viral infection specifically, since JAZ8-silenced plants do not display obvious developmental phenotypes nor present differences in their interaction with the viral insect vector. In summary, our results show that the geminivirus-encoded C2 interacts with JAZ8 in the nucleus, and suggest that this plant protein exerts an anti-geminiviral effect.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech