Title: Context Autoencoder for Self-Supervised Representation Learning. Abstract: We present a novel masked image modeling (MIM) approach, context autoencoder (CAE), for self-supervised representation pretraining. The goal is to pretrain an encoder by solving the pretext task: estimate the masked patches from the visible patches in an image. Our approach first feeds the visible patches into the encoder, extracting the representations. Then, we make predictions from visible patches to masked patches in the encoded representation space. We introduce an alignment constraint, encouraging that the representations for masked patches, predicted from the encoded representations of visible patches, are aligned with the masked patch presentations computed from the encoder. In other words, the predicted representations are expected to lie in the encoded representation space, which empirically shows the benefit to representation learning. Last, the predicted masked patch representations are mapped to the targets of the pretext task through a decoder.
One additional characteristic is that our approach encourages the separation of the representation learning part (encoder), and the pretext task completion part that will be replaced by the downstream task part. In contrast, previous MIM methods (e.g., BEiT and MAE) couple the two parts, potentially limiting the representation learning quality. We demonstrate the effectiveness of our CAE through superior transfer performance in downstream tasks: semantic segmentation, and object detection and instance segmentation.