Finding dense semantic correspondence is a fundamental problem in computer
vision, which remains challenging in complex scenes due to background clutter,
extreme intra-class variation, and a severe lack of ground truth. In this
paper, we aim to address the challenge of label sparsity in semantic
correspondence by enriching supervision signals from sparse keypoint
annotations. To this end, we first propose a teacher-student learning paradigm
for generating dense pseudo-labels and then develop two novel strategies for
denoising pseudo-labels. In particular, we use spatial priors around the sparse
annotations to suppress the noisy pseudo-labels. In addition, we introduce a
loss-driven dynamic label selection strategy for label denoising. We
instantiate our paradigm with two variants of learning strategies: a single
offline teacher setting, and mutual online teachers setting. Our approach
achieves notable improvements on three challenging benchmarks for semantic
correspondence and establishes the new state-of-the-art. Project page:
this https URL

本文针对语义对应中标签稀疏性的挑战，提出了一种基于稀疏关键点注释生成密集伪标签的教师 - 学生学习范式，并开发了两种噪声伪标签去噪策略，使用空间先验和损失驱动的动态标签选择策略。实验结果表明该方法在语义对应的三个基准测试中均取得了显著进展并建立了新的最新成果。

利用稀疏标注学习语义对应

Learning Semantic Correspondence with Sparse Annotations

We propose C3DPO, a method for extracting 3D models of deformable objects
from 2D keypoint annotations in unconstrained images. We do so by learning a
deep network that reconstructs a 3D object from a single view at a time,
accounting for partial occlusions, and explicitly factoring the effects of
viewpoint changes and object deformations. In order to achieve this
factorization, we introduce a novel regularization technique. We first show
that the factorization is successful if, and only if, there exists a certain
canonicalization function of the reconstructed shapes. Then, we learn the
canonicalization function together with the reconstruction one, which
constrains the result to be consistent. We demonstrate state-of-the-art
reconstruction results for methods that do not use ground-truth 3D supervision
for a number of benchmarks, including Up3D and PASCAL3D+. Source code has been
made available at this https URL

C3DPO 是一种从 2D 关键点注释中提取可变形物体的 3D 模型的方法，通过学习深度网络来重建三维物体，并明确因素化影响各个角度变化和物体变形，在不需要三维监督的情况下展示了一系列基准的最新重建结果。