Unified Transformer Tracker for Object Tracking

As an important area in computer vision, object tracking has formed two separate communities that respectively study Single Object Tracking (SOT) and Multiple Object Tracking (MOT). However, current methods in one tracking scenario are not easily adapted to the other due to the divergent training datasets and tracking objects of both tasks. Although UniTrack \cite{wang2021different} demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on single object tracking. In this work, we present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both SOT and MOT. The correlation between the target and tracking frame features is exploited to localize the target. We demonstrate that both SOT and MOT tasks can be solved within this framework. The model can be simultaneously end-to-end trained by alternatively optimizing the SOT and MOT objectives on the datasets of individual tasks. Extensive experiments are conducted on several benchmarks with a unified model trained on SOT and MOT datasets. Code will be available at https://github.com/Flowerfan/Trackron.Comment: CVPR 202

Biomass derived oligosaccharides for potential leather tanning

The global demand for renewable and affordable feedstocks, combined with the worldwide targets for reducing carbon emissions, is the driving force behind a breakthrough in resource revolution and GreenTech innovations..

Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion

Point clouds scanned by real-world sensors are always incomplete, irregular, and noisy, making the point cloud completion task become increasingly more important. Though many point cloud completion methods have been proposed, most of them require a large number of paired complete-incomplete point clouds for training, which is labor exhausted. In contrast, this paper proposes a novel Reconstruction-Aware Prior Distillation semi-supervised point cloud completion method named RaPD, which takes advantage of a two-stage training scheme to reduce the dependence on a large-scale paired dataset. In training stage 1, the so-called deep semantic prior is learned from both unpaired complete and unpaired incomplete point clouds using a reconstruction-aware pretraining process. While in training stage 2, we introduce a semi-supervised prior distillation process, where an encoder-decoder-based completion network is trained by distilling the prior into the network utilizing only a small number of paired training samples. A self-supervised completion module is further introduced, excavating the value of a large number of unpaired incomplete point clouds, leading to an increase in the network's performance. Extensive experiments on several widely used datasets demonstrate that RaPD, the first semi-supervised point cloud completion method, achieves superior performance to previous methods on both homologous and heterologous scenarios

Overexpression of luxS

LuxS/AI-2 quorum sensing (QS) system involves the production of cell signaling molecules via luxS-based autoinducer-2 (AI-2). LuxS has been reported to plays critical roles in regulating various behaviors of bacteria. AI-2 is a byproduct of the catabolism of S-adenosylhomocysteine (SAH) performed by the LuxS and Pfs enzymes. In our previous study, the function of LuxS in AI-2 production was verified in Streptococcus suis (SS). Decreased levels of SS biofilm formation and host-cell adherence as well as an inability to produce AI-2 were observed in bacteria having a luxS mutant gene. In this study, the level of AI-2 activity exhibits a growth-phase dependence with a maximum in late exponential culture in SS. An SS strain that overexpressed luxS was constructed to comprehensively understand the function of AI-2. Overexpressed luxS was not able to increase the level of pfs expression and produce additional AI-2, and the bacteria were slower growing and produced only slightly more biofilm than the wild type. Thus, AI-2 production is not correlated with luxS transcription. luxS expression is constitutive, but the transcription of pfs is perhaps correlated with AI-2 production in SS

Deep Clustering Survival Machines with Interpretable Expert Distributions

Conventional survival analysis methods are typically ineffective to characterize heterogeneity in the population while such information can be used to assist predictive modeling. In this study, we propose a hybrid survival analysis method, referred to as deep clustering survival machines, that combines the discriminative and generative mechanisms. Similar to the mixture models, we assume that the timing information of survival data is generatively described by a mixture of certain numbers of parametric distributions, i.e., expert distributions. We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions. This method also facilitates interpretable subgrouping/clustering of all instances according to their associated expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that the method is capable of obtaining promising clustering results and competitive time-to-event predicting performance