De-coupling and De-positioning Dense Self-supervised Learning

Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. Although the dense features extracted by employing segmentation maps and bounding boxes allow networks to perform SSL for each object, we show that they suffer from coupling and positional bias, which arise from the receptive field increasing with layer depth and zero-padding. We address this by introducing three data augmentation strategies, and leveraging Figure 1: Object coupling and positional bias on the them in (i) a decoupling module that aims to robustify region-level features extracted from a scene with multiple the network to variations in the object's surroundings, and objects. The overlap between objects increases with the (ii) a de-positioning module that encourages the network to depth within the network, thus mixing information across discard positional object information.