# Proposal Flow: Semantic Correspondences from Object Proposals

arXiv: https://arxiv.org/abs/1703.07144 · HTML: https://mlsanity.com/paper/1703.07144/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2017.

## Built on by (most cited first, 30 of 47)

- [Emergent Correspondence from Image Diffusion](https://mlsanity.com/paper/2306.03881/) (2023)
- [Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence](https://mlsanity.com/paper/2305.14334/) (2023)
- [Hypercorrelation Squeeze for Few-Shot Segmentation](https://mlsanity.com/paper/2104.01538/) (2021)
- [Unsupervised Semantic Correspondence Using Stable Diffusion](https://mlsanity.com/paper/2305.15581/) (2023)
- [Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation](https://mlsanity.com/paper/2207.10866/) (2022)
- [Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence](https://mlsanity.com/paper/2311.17034/) (2023)
- [CATs: Cost Aggregation Transformers for Visual Correspondence](https://mlsanity.com/paper/2106.02520/) (2021)
- [DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision](https://mlsanity.com/paper/2105.06464/) (2021)
- [CATs++: Boosting Cost Aggregation with Convolutions and Transformers](https://mlsanity.com/paper/2202.06817/) (2022)
- [Convolutional Hough Matching Networks](https://mlsanity.com/paper/2103.16831/) (2021)
- [Warp Consistency for Unsupervised Learning of Dense Correspondences](https://mlsanity.com/paper/2104.03308/) (2021)
- [SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching](https://mlsanity.com/paper/2310.17569/) (2023)
- [Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence](https://mlsanity.com/paper/2210.02689/) (2022)
- [Learning Semantic Correspondence with Sparse Annotations](https://mlsanity.com/paper/2208.06974/) (2022)
- [Deep Matching Prior: Test-Time Optimization for Dense Correspondence](https://mlsanity.com/paper/2106.03090/) (2021)
- [Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences](https://mlsanity.com/paper/2203.04279/) (2022)
- [TransforMatcher: Match-to-Match Attention for Semantic Correspondence](https://mlsanity.com/paper/2205.11634/) (2022)
- [ASIC: Aligning Sparse in-the-wild Image Collections](https://mlsanity.com/paper/2303.16201/) (2023)
- [Virtual Correspondence: Humans as a Cue for Extreme-View Geometry](https://mlsanity.com/paper/2206.08365/) (2022)
- [Diffusion Model for Dense Matching](https://mlsanity.com/paper/2305.19094/) (2023)
- [Distillation of Diffusion Features for Semantic Correspondence](https://mlsanity.com/paper/2412.03512/) (2024)
- [Cost Aggregation Is All You Need for Few-Shot Segmentation](https://mlsanity.com/paper/2112.11685/) (2021)
- [Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence](https://mlsanity.com/paper/2403.11120/) (2024)
- [Convolutional Hough Matching Networks for Robust and Efficient Visual Correspondence](https://mlsanity.com/paper/2109.05221/) (2021)
- [Learning Contrastive Representation for Semantic Correspondence](https://mlsanity.com/paper/2109.10967/) (2021)
- [SHIC: Shape-Image Correspondences with no Keypoint Supervision](https://mlsanity.com/paper/2407.18907/) (2024)
- [MATCHA:Towards Matching Anything](https://mlsanity.com/paper/2501.14945/) (2025)
- [Demystifying Unsupervised Semantic Correspondence Estimation](https://mlsanity.com/paper/2207.05054/) (2022)
- [Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence](https://mlsanity.com/paper/2209.08742/) (2022)
- [SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning](https://mlsanity.com/paper/2305.02385/) (2023)

## Abstract (arXiv)

Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout. Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals. We also show that the corresponding sparse proposal flow can effectively be transformed into a conventional dense flow field. We introduce two new challenging datasets that can be used to evaluate both general semantic flow techniques and region-based approaches such as proposal flow. We use these benchmarks to compare different matching algorithms, object proposals, and region features within proposal flow, to the state of the art in semantic flow. This comparison, along with experiments on standard datasets, demonstrates that proposal flow significantly outperforms existing semantic flow methods in various settings.