RGB-Infrared person re-identification (RGB-IR ReID) is a challenging
cross-modality retrieval problem, which aims at matching the person-of-interest
over visible and infrared camera views. Most existing works achieve performance
gains through manually-designed feature selection modules, which often require
significant domain knowledge and rich experience. In this paper, we study a
general paradigm, termed Neural Feature Search (NFS), to automate the process
of feature selection. Specifically, NFS combines a dual-level feature search
space and a differentiable search strategy to jointly select identity-related
cues in coarse-grained channels and fine-grained spatial pixels. This
combination allows NFS to adaptively filter background noises and concentrate
on informative parts of human bodies in a data-driven manner. Moreover, a
cross-modality contrastive optimization scheme further guides NFS to search
features that can minimize modality discrepancy whilst maximizing inter-class
distance. Extensive experiments on mainstream benchmarks demonstrate that our
method outperforms state-of-the-arts, especially achieving better performance
on the RegDB dataset with significant improvement of 11.20% and 8.64% in Rank-1
and mAP, respectively.

本文提出了一种称为神经特征搜索（NFS）的自动化特征选择范式来解决 RGB - 红外人员再识别方面的挑战性跨模态检索问题，通过对身体部位及数据驱动的自适应筛选等手段，实现了自动化特征选择的过程，进一步获得了更好的性能表现。

RGB - 红外人员重识别的神经特征搜索

Neural Feature Search for RGB-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging
cross-modality pedestrian retrieval problem. Due to the large intra-class
variations and cross-modality discrepancy with large amount of sample noise, it
is difficult to learn discriminative part features. Existing VI-ReID methods
instead tend to learn global representations, which have limited
discriminability and weak robustness to noisy images. In this paper, we propose
a novel dynamic dual-attentive aggregation (DDAG) learning method by mining
both intra-modality part-level and cross-modality graph-level contextual cues
for VI-ReID. We propose an intra-modality weighted-part attention module to
extract discriminative part-aggregated features, by imposing the domain
knowledge on the part relationship mining. To enhance robustness against noisy
samples, we introduce cross-modality graph structured attention to reinforce
the representation with the contextual relations across the two modalities. We
also develop a parameter-free dynamic dual aggregation learning strategy to
adaptively integrate the two components in a progressive joint training manner.
Extensive experiments demonstrate that DDAG outperforms the state-of-the-art
methods under various settings.

本文提出了动态双重注意聚合（DDAG）学习方法，通过挖掘 VI-ReID 中的模态内部部分级别和跨模态图级别的上下文线索，提取具有较强辨别力和噪声鲁棒性的聚合特征，从而提高了检索精度。

可见光 - 红外人员再识别的动态双关注聚合学习

Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person  Re-Identification

This paper demonstrates the feasibility of learning to retrieve short
snippets of sheet music (images) when given a short query excerpt of music
(audio) -- and vice versa --, without any symbolic representation of music or
scores. This would be highly useful in many content-based musical retrieval
scenarios. Our approach is based on Deep Canonical Correlation Analysis (DCCA)
and learns correlated latent spaces allowing for cross-modality retrieval in
both directions. Initial experiments with relatively simple monophonic music
show promising results.

本文旨在研究跨媒体检索中的乐曲检索，基于 Deep Canonical Correlation Analysis 学习相关空间，能够检索图片和音频之间的乐曲片段，初步实验结果有所进展。