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Text-guided Zero-Shot Object Localization

arXiv 2411.11357 ↗First posted 18 Nov 2024cs.CVcited by 0

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Abstract (arXiv)

Object localization is a hot issue in computer vision area, which aims to identify and determine the precise location of specific objects from image or video. Most existing object localization methods heavily rely on extensive labeled data, which are costly to annotate and constrain their applicability. Therefore, we propose a new Zero-Shot Object Localization (ZSOL) framework for addressing the aforementioned challenges. In the proposed framework, we introduce the Contrastive Language Image Pre-training (CLIP) module which could integrate visual and linguistic information effectively. Furthermore, we design a Text Self-Similarity Matching (TSSM) module, which could improve the localization accuracy by enhancing the representation of text features extracted by CLIP module. Hence, the proposed framework can be guided by prompt words to identify and locate specific objects in an image in the absence of labeled samples. The results of extensive experiments demonstrate that the proposed method could improve the localization performance significantly and establishes an effective benchmark for further research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 38 of its 55 references that are in the map. First posted on arXiv 18 Nov 2024.

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References · 38

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2023Segment Anythingcited by 8,234
  4. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  5. 2013Zero-Shot Learning Through Cross-Modal Transfercited by 237
  6. 2021Rethinking Counting and Localization in Crowds:A Purely Point-Based Frameworkcited by 152
  7. 2021Learning To Count Everythingcited by 150
  8. 2019Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Drivingcited by 105
  9. 2019Rethinking Classification and Localization for Object Detectioncited by 98
  10. 2022Self-Support Few-Shot Semantic Segmentationcited by 96
  11. 2021Generalized Few-Shot Object Detection without Forgettingcited by 88
  12. 2021Weakly Supervised Object Localization and Detection: A Surveycited by 84
26 more
  1. 2023CLIP-Count: Towards Text-Guided Zero-Shot Object Countingcited by 83
  2. 2018Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Featurescited by 81
  3. 2022Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Countingcited by 80
  4. 2024Local All-Pair Correspondence for Point Trackingcited by 80
  5. 2021Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detectioncited by 79
  6. 2022An End-to-End Transformer Model for Crowd Localizationcited by 74
  7. 2019Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detectioncited by 73
  8. 2016Finding Tiny Facescited by 72
  9. 2022A Low-Shot Object Counting Network With Iterative Prototype Adaptationcited by 65
  10. 2019Point in, Box out: Beyond Counting Persons in Crowdscited by 62
  11. 2022Few-shot Object Counting with Similarity-Aware Feature Enhancementcited by 62
  12. 2024CountGD: Multi-Modal Open-World Countingcited by 59
  13. 2022Few-shot Object Counting and Detectioncited by 55
  14. 2023VLCounter: Text-aware Visual Representation for Zero-Shot Object Countingcited by 45
  15. 2023STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learningcited by 41
  16. 2023Point, Segment and Count: A Generalized Framework for Object Countingcited by 39
  17. 2021Focal Inverse Distance Transform Maps for Crowd Localizationcited by 36
  18. 2020Learning Independent Instance Maps for Crowd Localizationcited by 24
  19. 2023DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Countingcited by 18
  20. 2015LocNet: Improving Localization Accuracy for Object Detectioncited by 14
  21. 2019UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markerscited by 12
  22. 2022Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teachercited by 12
  23. 2021Dense Point Prediction: A Simple Baseline for Crowd Counting and Localizationcited by 10
  24. 2019Metric Learning with Background Noise Class for Few-shot Detection of Rare Sound Eventscited by 4
  25. 2024Dense Center-Direction Regression for Object Counting and Localization with Point Supervisioncited by 3
  26. 2024Few-shot Object Localizationcited by 2

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