Match me if you can: Semantic Correspondence Learning with Unpaired
  Images
Jiwon Kim Byeongho Heo Sangdoo Yun Seungryong Kim Dongyoon Han
Recent approaches for semantic correspondence have focused on obtaining
high-quality correspondences using a complicated network, refining the
ambiguous or noisy matching points. Despite their performance improvements,
they remain constrained by the limited training pairs due to costly point-level
annotations. This paper proposes a simple yet effective method that performs
training with unlabeled pairs to complement both limited image pairs and sparse
point pairs, requiring neither extra labeled keypoints nor trainable modules.
We fundamentally extend the data quantity and variety by augmenting new
unannotated pairs not primitively provided as training pairs in benchmarks.
Using a simple teacher-student framework, we offer reliable pseudo
correspondences to the student network via machine supervision. Finally, the
performance of our network is steadily improved by the proposed iterative
training, putting back the student as a teacher to generate refined labels and
train a new student repeatedly. Our models outperform the milestone baselines,
including state-of-the-art methods on semantic correspondence benchmarks.