  In this paper, we introduce a novel self-supervised learning (SSL) loss for
image representation learning. There is a growing belief that generalization in
deep neural networks is linked to their ability to discriminate object shapes.
Since object shape is related to the location of its parts, we propose to
detect those that have been artificially misplaced. We represent object parts
with image tokens and train a ViT to detect which token has been combined with
an incorrect positional embedding. We then introduce sparsity in the inputs to
make the model more robust to occlusions and to speed up the training. We call
our method DILEMMA, which stands for Detection of Incorrect Location EMbeddings
with MAsked inputs. We apply DILEMMA to MoCoV3, DINO and SimCLR and show an
improvement in their performance of respectively 4.41%, 3.97%, and 0.5% under
the same training time and with a linear probing transfer on ImageNet-1K. We
also show full fine-tuning improvements of MAE combined with our method on
ImageNet-100. We evaluate our method via fine-tuning on common SSL benchmarks.
Moreover, we show that when downstream tasks are strongly reliant on shape
(such as in the YOGA-82 pose dataset), our pre-trained features yield a
significant gain over prior work.
