  We address the problem of learning self-supervised representations from
unlabeled image collections. Unlike existing approaches that attempt to learn
useful features by maximizing similarity between augmented versions of each
input image or by speculatively picking negative samples, we instead also make
use of the natural variation that occurs in image collections that are captured
using static monitoring cameras. To achieve this, we exploit readily available
context data that encodes information such as the spatial and temporal
relationships between the input images. We are able to learn representations
that are surprisingly effective for downstream supervised classification, by
first identifying high probability positive pairs at training time, i.e. those
images that are likely to depict the same visual concept. For the critical task
of global biodiversity monitoring, this results in image features that can be
adapted to challenging visual species classification tasks with limited human
supervision. We present results on four different camera trap image
collections, across three different families of self-supervised learning
methods, and show that careful image selection at training time results in
superior performance compared to existing baselines such as conventional
self-supervised training and transfer learning.
