Semantic Correspondence: Unified Benchmarking and a Strong Baseline

• URL: http://arxiv.org/abs/2505.18060v3
• Date: Tue, 27 May 2025 09:45:05 GMT
• Title: Semantic Correspondence: Unified Benchmarking and a Strong Baseline
• Authors: Kaiyan Zhang, Xinghui Li, Jingyi Lu, Kai Han,
• Abstract summary: We present the first extensive survey of semantic correspondence methods.<n>We aggregate and summarize the results of methods in literature across various benchmarks into a unified comparative table.<n>We propose a simple yet effective baseline that achieves state-of-the-art performance on multiple benchmarks.
• Score: 14.012377730820342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Establishing semantic correspondence is a challenging task in computer vision, aiming to match keypoints with the same semantic information across different images. Benefiting from the rapid development of deep learning, remarkable progress has been made over the past decade. However, a comprehensive review and analysis of this task remains absent. In this paper, we present the first extensive survey of semantic correspondence methods. We first propose a taxonomy to classify existing methods based on the type of their method designs. These methods are then categorized accordingly, and we provide a detailed analysis of each approach. Furthermore, we aggregate and summarize the results of methods in literature across various benchmarks into a unified comparative table, with detailed configurations to highlight performance variations. Additionally, to provide a detailed understanding on existing methods for semantic matching, we thoroughly conduct controlled experiments to analyse the effectiveness of the components of different methods. Finally, we propose a simple yet effective baseline that achieves state-of-the-art performance on multiple benchmarks, providing a solid foundation for future research in this field. We hope this survey serves as a comprehensive reference and consolidated baseline for future development. Code is publicly available at: https://github.com/Visual-AI/Semantic-Correspondence.

Related papers

• SLAck: Semantic, Location, and Appearance Aware Open-Vocabulary Tracking [89.43370214059955]
  Open-vocabulary Multiple Object Tracking (MOT) aims to generalize trackers to novel categories not in the training set. We present a unified framework that jointly considers semantics, location, and appearance priors in the early steps of association. Our method eliminates complex post-processings for fusing different cues and boosts the association performance significantly for large-scale open-vocabulary tracking.
  arXiv  Detail & Related papers  (2024-09-17T14:36:58Z)
• On the Evaluation Consistency of Attribution-based Explanations [42.1421504321572]
  We introduce Meta-Rank, an open platform for benchmarking attribution methods in the image domain. Our benchmark reveals three insights in attribution evaluation endeavors: 1) evaluating attribution methods under disparate settings can yield divergent performance rankings; 2) although inconsistent across numerous cases, the performance rankings exhibit remarkable consistency across distinct checkpoints along the same training trajectory; and 3) prior attempts at consistent evaluation fare no better than baselines when extended to more heterogeneous models and datasets.
  arXiv  Detail & Related papers  (2024-07-28T11:49:06Z)
• Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness [3.2925222641796554]
  "pointer-guided segment ordering" (SO) is a novel pre-training technique aimed at enhancing the contextual understanding of paragraph-level text representations. Our experiments show that pointer-guided pre-training significantly enhances the model's ability to understand complex document structures.
  arXiv  Detail & Related papers  (2024-06-06T15:17:51Z)
• Document Provenance and Authentication through Authorship Classification [5.2545206693029884]
  We propose an ensemble-based text-processing framework for the classification of single and multi-authored documents. The proposed framework incorporates several state-of-the-art text classification algorithms. The framework is evaluated on a large-scale benchmark dataset.
  arXiv  Detail & Related papers  (2023-03-02T12:26:03Z)
• 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)
• Fine-Grained Visual Entailment [51.66881737644983]
  We propose an extension of this task, where the goal is to predict the logical relationship of fine-grained knowledge elements within a piece of text to an image. Unlike prior work, our method is inherently explainable and makes logical predictions at different levels of granularity. We evaluate our method on a new dataset of manually annotated knowledge elements and show that our method achieves 68.18% accuracy at this challenging task.
  arXiv  Detail & Related papers  (2022-03-29T16:09:38Z)
• A Proposed Conceptual Framework for a Representational Approach to Information Retrieval [42.67826268399347]
  This paper outlines a conceptual framework for understanding recent developments in information retrieval and natural language processing. I propose a representational approach that breaks the core text retrieval problem into a logical scoring model and a physical retrieval model.
  arXiv  Detail & Related papers  (2021-10-04T15:57:02Z)
• Comprehensive Studies for Arbitrary-shape Scene Text Detection [78.50639779134944]
  We propose a unified framework for the bottom-up based scene text detection methods. Under the unified framework, we ensure the consistent settings for non-core modules. With the comprehensive investigations and elaborate analyses, it reveals the advantages and disadvantages of previous models.
  arXiv  Detail & Related papers  (2021-07-25T13:18:55Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)

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.