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SPair-71k: A Large-scale Benchmark for Semantic Correspondence

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arxiv 1908.10543 v1 pith:57EVUUHI submitted 2019-08-28 cs.CV

Juhong Min , Jongmin Lee , Jean Ponce , Minsu Cho This is my paper · ORCID
classification cs.CV
keywords semanticcorrespondencebenchmarkdatasetresearchspair-71kcontainsdatasets
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Establishing visual correspondences under large intra-class variations, which is often referred to as semantic correspondence or semantic matching, remains a challenging problem in computer vision. Despite its significance, however, most of the datasets for semantic correspondence are limited to a small amount of image pairs with similar viewpoints and scales. In this paper, we present a new large-scale benchmark dataset of semantically paired images, SPair-71k, which contains 70,958 image pairs with diverse variations in viewpoint and scale. Compared to previous datasets, it is significantly larger in number and contains more accurate and richer annotations. We believe this dataset will provide a reliable testbed to study the problem of semantic correspondence and will help to advance research in this area. We provide the results of recent methods on our new dataset as baselines for further research. Our benchmark is available online at http://cvlab.postech.ac.kr/research/SPair-71k/.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Weakly-Supervised Learning of Dense Functional Correspondences

    cs.CV 2025-09 conditional novelty 7.0 of 10

    A weakly-supervised pipeline that distills VLM functional part knowledge and multi-view spatial structure into a model for dense cross-category functional correspondence, outperforming baselines on new synthetic and r...

  2. Unsupervised Pixel-Level Semantic Left-Right Understanding of In-the-Wild Images

    cs.CV 2026-07 accept novelty 6.0 of 10

    An unsupervised hybrid 3D-shape + image framework produces dense pixel-level semantic left-right labels for objects in wild images, outperforming prior feature-based baselines even on unseen categories.

  3. Hidden in plain sight: VLMs overlook their visual representations

    cs.CV 2025-06 conditional novelty 6.0 of 10

    VLMs perform far worse than their own visual encoders on vision-centric tasks because the language model fails to use accessible visual information and instead follows its language priors.

  4. Semantic Correspondence: Unified Benchmarking and a Strong Baseline

    cs.CV 2025-05 conditional novelty 6.0 of 10

    Fine-tuning the last layers of DINOv2, optionally with a lightweight cost aggregator, yields state-of-the-art semantic correspondence accuracy, and a new survey and benchmark consolidate the field's results.

  5. Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Massive activations in DiTs are timestep-driven detail channels; suppressing them guides finer sampling and AdaLN-modulating them yields more discriminative dense features.

  6. Towards Robust Semantic Correspondence: A Benchmark and Insights

    cs.CV 2025-08 unverdicted novelty 4.0 of 10

    The abstract promises an adverse-condition benchmark for semantic correspondence, yet the full text is a GRB magnetar analysis, so the claimed benchmark is unverifiable.

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