  Most object recognition approaches predominantly focus on learning
discriminative visual patterns while overlooking the holistic object structure.
Though important, structure modeling usually requires significant manual
annotations and therefore is labor-intensive. In this paper, we propose to
"look into object" (explicitly yet intrinsically model the object structure)
through incorporating self-supervisions into the traditional framework. We show
the recognition backbone can be substantially enhanced for more robust
representation learning, without any cost of extra annotation and inference
speed. Specifically, we first propose an object-extent learning module for
localizing the object according to the visual patterns shared among the
instances in the same category. We then design a spatial context learning
module for modeling the internal structures of the object, through predicting
the relative positions within the extent. These two modules can be easily
plugged into any backbone networks during training and detached at inference
time. Extensive experiments show that our look-into-object approach (LIO)
achieves large performance gain on a number of benchmarks, including generic
object recognition (ImageNet) and fine-grained object recognition tasks (CUB,
Cars, Aircraft). We also show that this learning paradigm is highly
generalizable to other tasks such as object detection and segmentation (MS
COCO). Project page: https://github.com/JDAI-CV/LIO.
