# Contrastive Transformation for Self-supervised Correspondence Learning

arXiv: https://arxiv.org/abs/2012.05057 · HTML: https://mlsanity.com/paper/2012.05057/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 21 of 21)

- [Joint Inductive and Transductive Learning for Video Object Segmentation](https://mlsanity.com/paper/2108.03679/) (2021)
- [CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation](https://mlsanity.com/paper/2208.12448/) (2022)
- [Dense Unsupervised Learning for Video Segmentation](https://mlsanity.com/paper/2111.06265/) (2021)
- [Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning](https://mlsanity.com/paper/2203.14333/) (2022)
- [C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation](https://mlsanity.com/paper/2212.11078/) (2022)
- [Boosting Video Object Segmentation via Space-time Correspondence Learning](https://mlsanity.com/paper/2304.06211/) (2023)
- [Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation](https://mlsanity.com/paper/2112.01402/) (2021)
- [Deep Learning Technique for Human Parsing: A Survey and Outlook](https://mlsanity.com/paper/2301.00394/) (2023)
- [Modelling Neighbor Relation in Joint Space-Time Graph for Video Correspondence Learning](https://mlsanity.com/paper/2109.13499/) (2021)
- [Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation](https://mlsanity.com/paper/2303.10100/) (2023)
- [Spatial-then-Temporal Self-Supervised Learning for Video Correspondence](https://mlsanity.com/paper/2209.07778/) (2022)
- [Video Joint Modelling Based on Hierarchical Transformer for Co-summarization](https://mlsanity.com/paper/2112.13478/) (2021)
- [In-N-Out Generative Learning for Dense Unsupervised Video Segmentation](https://mlsanity.com/paper/2203.15312/) (2022)
- [Learning Fine-Grained Features for Pixel-wise Video Correspondences](https://mlsanity.com/paper/2308.03040/) (2023)
- [Description-Enhanced Label Embedding Contrastive Learning for Text Classification](https://mlsanity.com/paper/2306.08817/) (2023)
- [FlowFeat: Pixel-Dense Embedding of Motion Profiles](https://mlsanity.com/paper/2511.07696/) (2025)
- [How to Track and Segment Fish without Human Annotations: A Self-Supervised Deep Learning Approach](https://mlsanity.com/paper/2208.10662/) (2022)
- [I2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation](https://mlsanity.com/paper/2310.15568/) (2023)
- [Dynamic in Static: Hybrid Visual Correspondence for Self-Supervised Video Object Segmentation](https://mlsanity.com/paper/2404.13505/) (2024)
- [`Attention-Guided Cross-Temporal Clustering for Self-Supervised Video Object Segmentation](https://mlsanity.com/paper/2607.07230/) (2026)
- [SWT: Self-Supervised Video Object Segmentation via Sliding, Wavelet and Transportation](https://mlsanity.com/paper/2609.31725/) (2026)

## Abstract (arXiv)

In this paper, we focus on the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. The intra-video learning transforms the image contents across frames within a single video via the frame pair-wise affinity. To obtain the discriminative representation for instance-level separation, we go beyond the intra-video analysis and construct the inter-video affinity to facilitate the contrastive transformation across different videos. By forcing the transformation consistency between intra- and inter-video levels, the fine-grained correspondence associations are well preserved and the instance-level feature discrimination is effectively reinforced. Our simple framework outperforms the recent self-supervised correspondence methods on a range of visual tasks including video object tracking (VOT), video object segmentation (VOS), pose keypoint tracking, etc. It is worth mentioning that our method also surpasses the fully-supervised affinity representation (e.g., ResNet) and performs competitively against the recent fully-supervised algorithms designed for the specific tasks (e.g., VOT and VOS).