Mutimodal Ranking Optimization for Heterogeneous Face Re-identification

Heterogeneous face re-identification, namely matching heterogeneous faces across disjoint visible light (VIS) and near-infrared (NIR) cameras, has become an important problem in video surveillance application. However, the large domain discrepancy between heterogeneous NIR-VIS faces makes the performance of face re-identification degraded dramatically. To solve this problem, a multimodal fusion ranking optimization algorithm for heterogeneous face re-identification is proposed in this paper. Firstly, we design a heterogeneous face translation network to obtain multimodal face pairs, including NIR-VIS/NIR-NIR/VIS-VIS face pairs, through mutual transformation between NIR-VIS faces. Secondly, we propose linear and non-linear fusion strategies to aggregate initial ranking lists of multimodal face pairs and acquire the optimized re-ranked list based on modal complementarity. The experimental results show that the proposed multimodal fusion ranking optimization algorithm can effectively utilize the complementarity and outperforms some relative methods on the SCface dataset

Baryons in the light-front approach: the three-quark picture

In this work, a three-quark picture is constructed using a bottom-up approach for baryons in light-front quark model. The shape parameters, which characterize the momentum distribution inside a baryon, are determined with the help of the pole residue of the baryon. The relation between the three-quark picture and the diquark picture is clarified. In model construction, we find that Lorentz boost effect plays an important role, and the bottom-up modeling approach can be generalized to multiquark states. Based on this, a unified theoretical framework for describing multiquark states may be established. As a by-product of model construction, we can easily obtain a new and possibly better definition of baryon interpolating current. The hadron interpolating currents are the starting point of Lattice QCD and QCD sum rules, and therefore have important significance.Comment: 17 page

VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models

Diffusion models have achieved significant success in image and video generation. This motivates a growing interest in video editing tasks, where videos are edited according to provided text descriptions. However, most existing approaches only focus on video editing for short clips and rely on time-consuming tuning or inference. We are the first to propose Video Instruction Diffusion (VIDiff), a unified foundation model designed for a wide range of video tasks. These tasks encompass both understanding tasks (such as language-guided video object segmentation) and generative tasks (video editing and enhancement). Our model can edit and translate the desired results within seconds based on user instructions. Moreover, we design an iterative auto-regressive method to ensure consistency in editing and enhancing long videos. We provide convincing generative results for diverse input videos and written instructions, both qualitatively and quantitatively. More examples can be found at our website https://ChenHsing.github.io/VIDiff

Gap Anisotropy in Iron-Based Superconductors: A Point-Contact Andreev Reflection Study of BaFe2−x_{2-x}Nix_{x}As2_2 Single Crystals

We report a systematic investigation on c-axis point-contact Andreev reflection (PCAR) in BaFe$_{2-x}$Ni$_x$As$_2$ superconducting single crystals from underdoped to overdoped regions (0.075 $\leq x\leq 0.15$). At optimal doping ($x=0.1$) the PCAR spectrum feature the structures of two superconducting gap and electron-boson coupling mode. In the $s\pm$ scenario, quantitative analysis using a generalized Blonder-Tinkham-Klapwijk (BTK) formalism with two gaps: one isotropic and another angle dependent, suggest a nodeless state in strong-coupling limit with gap minima on the Fermi surfaces. Upon crossing above the optimal doping ($x > 0.1$), the PCAR spectrum show an in-gap sharp narrow peak at low bias, in contrast to the case of underdoped samples ($x < 0.1$), signaling the onset of deepened gap minima or nodes in the superconducting gap. This result provides evidence of the modulation of the gap amplitude with doping concentration, consistent with the calculations for the orbital dependent pair interaction mediated by the antiferromagnetic spin fluctuations.Comment: 5 pages, 4 figure