Learning embeddings that are invariant to the pose of the object is crucial
in visual image retrieval and re-identification. The existing approaches for
person, vehicle, or animal re-identification tasks suffer from high intra-class
variance due to deformable shapes and different camera viewpoints. To overcome
this limitation, we propose to align the image embedding with a predefined
order of the keypoints. The proposed keypoint aligned embeddings model
(KAE-Net) learns part-level features via multi-task learning which is guided by
keypoint locations. More specifically, KAE-Net extracts channels from a feature
map activated by a specific keypoint through learning the auxiliary task of
heatmap reconstruction for this keypoint. The KAE-Net is compact, generic and
conceptually simple. It achieves state of the art performance on the benchmark
datasets of CUB-200-2011, Cars196 and VeRi-776 for retrieval and
re-identification tasks.

本研究提出了一种新的关键点对齐图像嵌入方法 (KAE-Net)，通过多任务学习和热力图重构来辅助训练，实现了图像的检索和重新识别任务，并在 CUB-200-2011、Cars196 和 VeRi-776 等基准数据集上取得了最先进的性能表现。

针对图像检索和重新识别的对齐关键点嵌入

Keypoint-Aligned Embeddings for Image Retrieval and Re-identification

We propose the first direct end-to-end multi-person pose estimation
framework, termed DirectPose. Inspired by recent anchor-free object detectors,
which directly regress the two corners of target bounding-boxes, the proposed
framework directly predicts instance-aware keypoints for all the instances from
a raw input image, eliminating the need for heuristic grouping in bottom-up
methods or bounding-box detection and RoI operations in top-down ones. We also
propose a novel Keypoint Alignment (KPAlign) mechanism, which overcomes the
main difficulty: lack of the alignment between the convolutional features and
predictions in this end-to-end framework. KPAlign improves the framework's
performance by a large margin while still keeping the framework end-to-end
trainable. With the only postprocessing non-maximum suppression (NMS), our
proposed framework can detect multi-person keypoints with or without
bounding-boxes in a single shot. Experiments demonstrate that the end-to-end
paradigm can achieve competitive or better performance than previous strong
baselines, in both bottom-up and top-down methods. We hope that our end-to-end
approach can provide a new perspective for the human pose estimation task.

提出了第一个直接的端到端多人姿态估计框架，称为 DirectPose，使用新的关键点对齐机制 (KPAlign)，该机制提高了准确性，并消除了需在底部向上方法中处理的启发式分组或在自顶向下方法中进行边界框检测和 RoI 操作。实验证明，这一端到端模式在人体姿态估计任务中可以达到与之前强大基线相媲美或更好的表现。