3D reconstruction of novel categories based on few-shot learning is appealing
in real-world applications and attracts increasing research interests. Previous
approaches mainly focus on how to design shape prior models for different
categories. Their performance on unseen categories is not very competitive. In
this paper, we present a Memory Prior Contrastive Network (MPCN) that can store
shape prior knowledge in a few-shot learning based 3D reconstruction framework.
With the shape memory, a multi-head attention module is proposed to capture
different parts of a candidate shape prior and fuse these parts together to
guide 3D reconstruction of novel categories. Besides, we introduce a 3D-aware
contrastive learning method, which can not only complement the retrieval
accuracy of memory network, but also better organize image features for
downstream tasks. Compared with previous few-shot 3D reconstruction methods,
MPCN can handle the inter-class variability without category annotations.
Experimental results on a benchmark synthetic dataset and the Pascal3D+
real-world dataset show that our model outperforms the current state-of-the-art
methods significantly.

本文提出了一种基于 MPCN 框架的 3D 重建方法，可以存储形状先验知识以进行新类别的重建，并引入多头注意力模块和 3D 感知对比学习方法，相比先前方法具有更好的可视化效果。

基于记忆先验对少样本单视图 3D 重建的对比网络

Few-shot Single-view 3D Reconstruction with Memory Prior Contrastive  Network

Text-based person search aims to retrieve images of a certain pedestrian by a
textual description. The key challenge of this task is to eliminate the
inter-modality gap and achieve the feature alignment across modalities. In this
paper, we propose a semantic-aligned embedding method for text-based person
search, in which the feature alignment across modalities is achieved by
automatically learning the semantic-aligned visual features and textual
features. First, we introduce two Transformer-based backbones to encode robust
feature representations of the images and texts. Second, we design a
semantic-aligned feature aggregation network to adaptively select and aggregate
features with the same semantics into part-aware features, which is achieved by
a multi-head attention module constrained by a cross-modality part alignment
loss and a diversity loss. Experimental results on the CUHK-PEDES and Flickr30K
datasets show that our method achieves state-of-the-art performances.

本文提出了一种基于语义对齐的嵌入方法，采用多头注意力模块和特征聚合网络进行特征对齐，以实现对于文本描述的行人图像搜索，并在 CUHK-PEDES 和 Flickr30K 数据集上取得了最先进的性能。