Revisit of directed flow in relativistic heavy-ion collisions from a multiphase transport model

We have revisited several interesting questions on how the rapidity-odd directed flow is developed in relativistic $^{197}$Au+$^{197}$Au collisions at $\sqrt{s_{NN}}$ = 200 and 39 GeV based on a multiphase transport model. As the partonic phase evolves with time, the slope of the parton directed flow at midrapidity region changes from negative to positive as a result of the later dynamics at 200 GeV, while it remains negative at 39 GeV due to the shorter life time of the partonic phase. The directed flow splitting for various quark species due to their different initial eccentricities is observed at 39 GeV, while the splitting is very small at 200 GeV. From a dynamical coalescence algorithm with Wigner functions, we found that the directed flow of hadrons is a result of competition between the coalescence in momentum and coordinate space as well as further modifications by the hadronic rescatterings.Comment: 8 pages, 8 figures, version after major revisio

Cluster-guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) aims to match pedestrian images from different camera views in unsupervised setting. Existing methods for unsupervised person Re-ID are usually built upon the pseudo labels from clustering. However, the quality of clustering depends heavily on the quality of the learned features, which are overwhelmingly dominated by the colors in images especially in the unsupervised setting. In this paper, we propose a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which cluster structure is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework. To be specific, we propose a novel cluster-level contrastive loss to help the siamese network effectively mine the invariance in feature learning with respect to the cluster structure within and between different data augmentation views, respectively. Extensive experiments conducted on three benchmark datasets demonstrate superior performance of our proposal

MaskCL: Semantic Mask-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change

This paper considers a novel and challenging problem: unsupervised long-term person re-identification with clothes change. Unfortunately, conventional unsupervised person re-id methods are designed for short-term cases and thus fail to perceive clothes-independent patterns due to simply being driven by RGB prompt. To tackle with such a bottleneck, we propose a semantic mask-driven contrastive learning approach, in which silhouette masks are embedded into contrastive learning framework as the semantic prompts and cross-clothes invariance is learnt from hierarchically semantic neighbor structure by combining both RGB and semantic features in a two-branches network. Since such a challenging re-id task setting is investigated for the first time, we conducted extensive experiments to evaluate state-of-the-art unsupervised short-term person re-id methods on five widely-used clothes-change re-id datasets. Experimental results verify that our approach outperforms the unsupervised re-id competitors by a clear margin, remaining a narrow gap to the supervised baselines