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TopicFM: Robust and Interpretable Topic-Assisted Feature Matching (2207.00328v3)

Published 1 Jul 2022 in cs.CV

Abstract: This study addresses an image-matching problem in challenging cases, such as large scene variations or textureless scenes. To gain robustness to such situations, most previous studies have attempted to encode the global contexts of a scene via graph neural networks or transformers. However, these contexts do not explicitly represent high-level contextual information, such as structural shapes or semantic instances; therefore, the encoded features are still not sufficiently discriminative in challenging scenes. We propose a novel image-matching method that applies a topic-modeling strategy to encode high-level contexts in images. The proposed method trains latent semantic instances called topics. It explicitly models an image as a multinomial distribution of topics, and then performs probabilistic feature matching. This approach improves the robustness of matching by focusing on the same semantic areas between the images. In addition, the inferred topics provide interpretability for matching the results, making our method explainable. Extensive experiments on outdoor and indoor datasets show that our method outperforms other state-of-the-art methods, particularly in challenging cases. The code is available at https://github.com/TruongKhang/TopicFM.

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References (63)
  1. HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. In CVPR, 5173–5182.
  2. Gan dissection: Visualizing and understanding generative adversarial networks. arXiv preprint arXiv:1811.10597.
  3. Speeded-up robust features (SURF). Computer vision and image understanding, 110(3): 346–359.
  4. Reinforced feature points: Optimizing feature detection and description for a high-level task. In CVPR, 4948–4957.
  5. Gms: Grid-based motion statistics for fast, ultra-robust feature correspondence. In CVPR, 4181–4190.
  6. Latent dirichlet allocation. Journal of machine Learning research, 3(Jan): 993–1022.
  7. Brief: Binary robust independent elementary features. In ECCV, 778–792. Springer.
  8. Transformer interpretability beyond attention visualization. In CVPR, 782–791.
  9. Learning to match features with seeded graph matching network. In ICCV, 6301–6310.
  10. Visual categorization with bags of keypoints. In Workshop on statistical learning in computer vision, ECCV, volume 1, 1–2. Prague.
  11. Cuturi, M. 2013. Sinkhorn distances: Lightspeed computation of optimal transport. NeurIPS, 26.
  12. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In CVPR, 5828–5839.
  13. Superpoint: Self-supervised interest point detection and description. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 224–236.
  14. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
  15. D2-net: A trainable cnn for joint description and detection of local features. In CVPR, 8092–8101.
  16. Effect of Parameter Optimization on Classical and Learning-based Image Matching Methods. In CVPR, 2506–2513.
  17. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6): 381–395.
  18. Detecting interpretable and accurate scale-invariant keypoints. In ICCV, 2256–2263. IEEE.
  19. Multiple view geometry in computer vision. Cambridge university press.
  20. Mask r-cnn. In CVPR, 2961–2969.
  21. Robust image retrieval-based visual localization using kapture. arXiv preprint arXiv:2007.13867.
  22. Cotr: Correspondence transformer for matching across images. In CVPR, 6207–6217.
  23. Dual-resolution correspondence networks. NeurIPS, 33: 17346–17357.
  24. Megadepth: Learning single-view depth prediction from internet photos. In CVPR, 2041–2050.
  25. Feature pyramid networks for object detection. In CVPR, 2117–2125.
  26. Microsoft coco: Common objects in context. In ECCV, 740–755. Springer.
  27. CODE: Coherence based decision boundaries for feature correspondence. TPAMI, 40(1): 34–47.
  28. Fully convolutional networks for semantic segmentation. In CVPR, 3431–3440.
  29. Lowe, D. G. 2004. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2): 91–110.
  30. Contextdesc: Local descriptor augmentation with cross-modality context. In CVPR, 2527–2536.
  31. Aslfeat: Learning local features of accurate shape and localization. In CVPR, 6589–6598.
  32. Image matching from handcrafted to deep features: A survey. International Journal of Computer Vision, 129(1): 23–79.
  33. Image stylization for robust features. arXiv preprint arXiv:2008.06959.
  34. Scalable nearest neighbor algorithms for high dimensional data. TPAMI, 36(11): 2227–2240.
  35. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE transactions on robotics, 31(5): 1147–1163.
  36. LF-Net: Learning local features from images. NeurIPS, 31.
  37. R2D2: repeatable and reliable detector and descriptor. arXiv preprint arXiv:1906.06195.
  38. Efficient neighbourhood consensus networks via submanifold sparse convolutions. In ECCV, 605–621. Springer.
  39. Neighbourhood consensus networks. NeurIPS, 31.
  40. ORB: An efficient alternative to SIFT or SURF. In ICCV, 2564–2571. Ieee.
  41. From coarse to fine: Robust hierarchical localization at large scale. In CVPR, 12716–12725.
  42. Superglue: Learning feature matching with graph neural networks. In CVPR, 4938–4947.
  43. Improving image-based localization by active correspondence search. In ECCV, 752–765. Springer.
  44. Grad-cam: Visual explanations from deep networks via gradient-based localization. In ICCV, 618–626.
  45. Efficient attention: Attention with linear complexities. In WACV, 3531–3539.
  46. ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching. In CVPR, 12517–12526.
  47. Video Google: A text retrieval approach to object matching in videos. In ICCV, volume 3, 1470–1470. IEEE Computer Society.
  48. Segmenter: Transformer for semantic segmentation. In ICCV, 7262–7272.
  49. LoFTR: Detector-free local feature matching with transformers. In CVPR, 8922–8931.
  50. InLoc: Indoor visual localization with dense matching and view synthesis. In CVPR, 7199–7209.
  51. DISK: Learning local features with policy gradient. NeurIPS, 33: 14254–14265.
  52. Interpretable image recognition by constructing transparent embedding space. In ICCV, 895–904.
  53. MatchFormer: Interleaving Attention in Transformers for Feature Matching. arXiv preprint arXiv:2203.09645.
  54. Explainable face recognition. In ECCV, 248–263. Springer.
  55. A biterm topic model for short texts. In Proceedings of the 22nd international conference on World Wide Web, 1445–1456.
  56. Lift: Learned invariant feature transform. In ECCV, 467–483. Springer.
  57. Learning to find good correspondences. In CVPR, 2666–2674.
  58. Object-contextual representations for semantic segmentation. In ECCV, 173–190. Springer.
  59. Learning two-view correspondences and geometry using order-aware network. In ICCV, 5845–5854.
  60. Reference pose generation for long-term visual localization via learned features and view synthesis. International Journal of Computer Vision, 129(4): 821–844.
  61. Towards interpretable deep metric learning with structural matching. In ICCV, 9887–9896.
  62. Learning deep features for discriminative localization. In CVPR, 2921–2929.
  63. Patch2pix: Epipolar-guided pixel-level correspondences. In CVPR, 4669–4678.
Citations (25)
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Summary

  • The paper introduces TopicFM, which employs a topic-modeling strategy to robustly match features by capturing high-level semantic context.
  • Methodologically, it integrates a coarse-to-fine architecture with UNet-like feature extraction and topic-assisted modules for precise pixel-level correspondences.
  • Evaluations on benchmarks like HPatches, MegaDepth, and Aachen Day-Night show TopicFM achieving superior AUC metrics and robust visual localization performance.

An Analysis of TopicFM: Robust and Interpretable Topic-Assisted Feature Matching

The paper introduces TopicFM, a novel approach to feature matching in images, addressing challenges posed by large scene variations and textureless environments. Unlike conventional methods utilizing graph neural networks or transformers to encode global contexts that often fail to represent high-level contextual information, TopicFM employs a topic-modeling strategy. This ensures high-level context representation, such as structural shapes or semantic instances, thereby enhancing the discriminative power of features in challenging image scenarios.

Methodological Insights

TopicFM diverges from typical feature matching processes by using latent semantic instances (topics) to model each image as a multinomial distribution over these topics. The innovative aspect of TopicFM lies in its topic-assisted feature matching approach that facilitates probabilistic feature matching, thereby increasing robustness by focusing on semantically similar areas between images.

The method is structured around a coarse-to-fine architecture:

  1. Feature Extraction: A UNet-like architecture generates multiscale dense features.
  2. Coarse-level Matching: The method uses a topic-assisted module to estimate matching probabilities and determine coarse correspondences. Here, the incorporation of latent topics inferred through transformers provides a robust, contextually rich feature set for matching.
  3. Fine-level Refinement: Similar to LoFTR, coarse matches are refined using high-resolution feature maps, achieving high precision in pixel-level correspondences.

TopicFM's strength lies in augmenting local visual features with topic information, thereby enhancing feature distinctiveness and interpretability by focusing on covisible topics during the matching process.

Comparative Evaluation and Performance

In benchmarking tests, TopicFM demonstrated superior performance over state-of-the-art methods in various challenging scenarios. When evaluated on HPatches for homography estimation, TopicFM outperformed other models, achieving higher AUC metrics across all pixel thresholds, particularly in the most challenging scenarios.

For relative pose estimation on MegaDepth and ScanNet, the results were comparably high, underscoring TopicFM's capability in delivering precise camera pose transformations even in texture-poor environments. Notably, TopicFM also excelled in visual localization tasks on datasets like Aachen Day-Night and InLoc, achieving top-tier results without dataset-specific fine-tuning, which highlights its robustness and versatility.

Interpretability and Efficiency

A significant advantage of using a topic-modeling approach is interpretability, which mirrors human cognitive processes in recognizing structures based on semantic information. The method visualizes topics that group semantically similar areas, thus providing an intuitive interpretation of matching results.

Efficiency is another important aspect of TopicFM, achieved through a streamlined end-to-end network design. By adopting a lightweight network and focusing on semantically rich areas, the method optimizes resource usage without sacrificing accuracy, making it suitable for real-time applications.

Implications and Future Directions

From a theoretical perspective, TopicFM illustrates the potential of integrating semantic modeling techniques from data mining into computer vision tasks. Practically, its success opens avenues for real-time applications, such as SLAM and augmented reality, where robust and interpretable feature matching is crucial.

Looking forward, further exploration could focus on the scalability of the topic model and its application to a broader range of scenarios. Additionally, integrating TopicFM with more complex image and video datasets could unveil its potential in more dynamic environments, thus broadening its utility in real-world applications. The research also suggests potential synergy with ongoing developments in self-supervised learning, which could provide a foundation for more adaptable and generalized feature representation models.

In summary, TopicFM represents a significant step in the quest for more robust and interpretable computer vision methods, demonstrating impressive performance and offering pivotal insights into feature matching mechanisms.

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