Demystifying Unsupervised Semantic Correspondence Estimation

• URL: http://arxiv.org/abs/2207.05054v1
• Date: Mon, 11 Jul 2022 17:59:51 GMT
• Title: Demystifying Unsupervised Semantic Correspondence Estimation
• Authors: Mehmet Ayg\"un and Oisin Mac Aodha
• Abstract summary: We explore semantic correspondence estimation through the lens of unsupervised learning. We thoroughly evaluate several recently proposed unsupervised methods across multiple challenging datasets. We introduce a new unsupervised correspondence approach which utilizes the strength of pre-trained features while encouraging better matches during training.
• Score: 13.060538447838303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We explore semantic correspondence estimation through the lens of unsupervised learning. We thoroughly evaluate several recently proposed unsupervised methods across multiple challenging datasets using a standardized evaluation protocol where we vary factors such as the backbone architecture, the pre-training strategy, and the pre-training and finetuning datasets. To better understand the failure modes of these methods, and in order to provide a clearer path for improvement, we provide a new diagnostic framework along with a new performance metric that is better suited to the semantic matching task. Finally, we introduce a new unsupervised correspondence approach which utilizes the strength of pre-trained features while encouraging better matches during training. This results in significantly better matching performance compared to current state-of-the-art methods.

Related papers

• Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation [6.979979613916754]
  News recommendation is a challenging task that involves personalization based on the interaction history and preferences of each user. Recent works have leveraged the power of pretrained language models (PLMs) to directly rank news items by using inference approaches that predominately fall into three categories: pointwise, pairwise, and listwise learning-to-rank. We propose a novel framework for PLM-based news recommendation that integrates both pointwise relevance prediction and pairwise comparisons in a scalable manner.
  arXiv  Detail & Related papers  (2024-09-26T10:27:19Z)
• OTMatch: Improving Semi-Supervised Learning with Optimal Transport [2.4355694259330467]
  We present a new approach called OTMatch, which leverages semantic relationships among classes by employing an optimal transport loss function to match distributions. The empirical results show improvements in our method above baseline, this demonstrates the effectiveness and superiority of our approach in harnessing semantic relationships to enhance learning performance in a semi-supervised setting.
  arXiv  Detail & Related papers  (2023-10-26T15:01:54Z)
• Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning [146.11600461034746]
  Method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. We prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
  arXiv  Detail & Related papers  (2022-09-27T19:04:36Z)
• Towards Better Understanding Attribution Methods [77.1487219861185]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We also propose a post-processing smoothing step that significantly improves the performance of some attribution methods.
  arXiv  Detail & Related papers  (2022-05-20T20:50:17Z)
• FineDiving: A Fine-grained Dataset for Procedure-aware Action Quality Assessment [93.09267863425492]
  We argue that understanding both high-level semantics and internal temporal structures of actions in competitive sports videos is the key to making predictions accurate and interpretable. We construct a new fine-grained dataset, called FineDiving, developed on diverse diving events with detailed annotations on action procedures.
  arXiv  Detail & Related papers  (2022-04-07T17:59:32Z)
• Imitation Learning by State-Only Distribution Matching [2.580765958706854]
  Imitation Learning from observation describes policy learning in a similar way to human learning. We propose a non-adversarial learning-from-observations approach, together with an interpretable convergence and performance metric.
  arXiv  Detail & Related papers  (2022-02-09T08:38:50Z)
• Revisiting Consistency Regularization for Semi-Supervised Learning [80.28461584135967]
  We propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss. Experimental results show that our model defines a new state of the art for various datasets and settings.
  arXiv  Detail & Related papers  (2021-12-10T20:46:13Z)
• Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation [48.294903659573585]
  In this paper, we propose to embed affinity learning of multi-stage approaches in a single-stage model. A deep neural network is used to deliver comprehensive semantic information in the training phase. Experiments are conducted on the PASCAL VOC 2012 dataset to evaluate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2021-08-03T07:48:33Z)
• Self-Supervised Prototypical Transfer Learning for Few-Shot Classification [11.96734018295146]
  Self-supervised transfer learning approach ProtoTransfer outperforms state-of-the-art unsupervised meta-learning methods on few-shot tasks. In few-shot experiments with domain shift, our approach even has comparable performance to supervised methods, but requires orders of magnitude fewer labels.
  arXiv  Detail & Related papers  (2020-06-19T19:00:11Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.