  We present Contextualized Local Visual Embeddings (CLoVE), a self-supervised
convolutional-based method that learns representations suited for dense
prediction tasks. CLoVE deviates from current methods and optimizes a single
loss function that operates at the level of contextualized local embeddings
learned from output feature maps of convolution neural network (CNN) encoders.
To learn contextualized embeddings, CLoVE proposes a normalized mult-head
self-attention layer that combines local features from different parts of an
image based on similarity. We extensively benchmark CLoVE's pre-trained
representations on multiple datasets. CLoVE reaches state-of-the-art
performance for CNN-based architectures in 4 dense prediction downstream tasks,
including object detection, instance segmentation, keypoint detection, and
dense pose estimation.
