This paper presents Contrastive Reconstruction, ConRec - a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss. We showcase that state-of-the-art contrastive learning methods (e.g. SimCLR) have shortcomings to capture fine-grained visual features in their representations. ConRec extends the SimCLR framework by adding (1) a self-reconstruction task and (2) an attention mechanism within the contrastive learning task. This is accomplished by applying a simple encoder-decoder architecture with two heads. We show that both extensions contribute towards an improved vector representation for images with fine-grained visual features. Combining those concepts, ConRec outperforms SimCLR and SimCLR with Attention-Pooling on fine-grained classification datasets.

本文提出对比重构（ConRec）算法，它通过联合优化对比和自重构损失获得图像表示，并添加了一个自重构任务和一个在对比学习任务中的注意机制以改进捕捉图像的细粒度视觉特征，证明该算法在捕捉细粒度视觉特征方面优于SimCLR。

通过结合对比学习、图像重建和注意力加权汇聚，实现细粒度视觉表征