mlsanityData to 7 Oct 2026

Phrase Decoupling Cross-Modal Hierarchical Matching and Progressive Position Correction for Visual Grounding

arXiv 2410.23570 ↗First posted 31 Oct 2024cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Visual grounding has attracted wide attention thanks to its broad application in various visual language tasks. Although visual grounding has made significant research progress, existing methods ignore the promotion effect of the association between text and image features at different hierarchies on cross-modal matching. This paper proposes a Phrase Decoupling Cross-Modal Hierarchical Matching and Progressive Position Correction Visual Grounding method. It first generates a mask through decoupled sentence phrases, and a text and image hierarchical matching mechanism is constructed, highlighting the role of association between different hierarchies in cross-modal matching. In addition, a corresponding target object position progressive correction strategy is defined based on the hierarchical matching mechanism to achieve accurate positioning for the target object described in the text. This method can continuously optimize and adjust the bounding box position of the target object as the certainty of the text description of the target object improves. This design explores the association between features at different hierarchies and highlights the role of features related to the target object and its position in target positioning. The proposed method is validated on different datasets through experiments, and its superiority is verified by the performance comparison with the state-of-the-art methods.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 52 of its 74 references that are in the map. First posted on arXiv 31 Oct 2024.

Builds on

52 of 74 references.

References · 52

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  4. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  7. 2017Mask R-CNNcited by 7,259
  8. 2015Fast R-CNNcited by 2,930
  9. 2018YOLOv3: An Incremental Improvementcited by 2,652
  10. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  11. 2016Modeling Context in Referring Expressionscited by 1,054
  12. 2015Generation and Comprehension of Unambiguous Object Descriptionscited by 897
38 more
  1. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
  2. 2023GLIGEN: Open-Set Grounded Text-to-Image Generationcited by 774
  3. 2018MAttNet: Modular Attention Network for Referring Expression Comprehensioncited by 427
  4. 2021TransVG: End-to-End Visual Grounding with Transformerscited by 269
  5. 2020Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentationcited by 204
  6. 2021Scaling Up Vision-Language Pre-training for Image Captioningcited by 196
  7. 2019A Fast and Accurate One-Stage Approach to Visual Groundingcited by 194
  8. 2023Dense Text-to-Image Generation with Attention Modulationcited by 162
  9. 2018Learning to Assemble Neural Module Tree Networks for Visual Groundingcited by 139
  10. 2022SeqTR: A Simple yet Universal Network for Visual Groundingcited by 138
  11. 2020Improving One-stage Visual Grounding by Recursive Sub-query Constructioncited by 137
  12. 2021LAFITE: Towards Language-Free Training for Text-to-Image Generationcited by 126
  13. 2022RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Datacited by 121
  14. 2016Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challengecited by 116
  15. 2019A Real-Time Cross-modality Correlation Filtering Method for Referring Expression Comprehensioncited by 111
  16. 2019Dynamic Graph Attention for Referring Expression Comprehensioncited by 111
  17. 2016Modeling Relationships in Referential Expressions with Compositional Modular Networkscited by 106
  18. 2019Learning to Compose and Reason with Language Tree Structures for Visual Groundingcited by 98
  19. 2019Improving Referring Expression Grounding with Cross-modal Attention-guided Erasingcited by 88
  20. 2022Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoningcited by 69
  21. 2017Parallel Attention: A Unified Framework for Visual Object Discovery through Dialogs and Queriescited by 56
  22. 2022Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual Groundingcited by 52
  23. 2021Look Before You Leap: Learning Landmark Features for One-Stage Visual Groundingcited by 51
  24. 2020Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Groundingcited by 50
  25. 2021Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformerscited by 49
  26. 2021Visual Grounding with Transformerscited by 42
  27. 2022TransVG++: End-to-End Visual Grounding with Language Conditioned Vision Transformercited by 41
  28. 2022SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answeringcited by 38
  29. 2022MuKEA: Multimodal Knowledge Extraction and Accumulation for Knowledge-based Visual Question Answeringcited by 36
  30. 2023CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Groundingcited by 35
  31. 2022Comprehending and Ordering Semantics for Image Captioningcited by 33
  32. 2019A Real-time Global Inference Network for One-stage Referring Expression Comprehensioncited by 32
  33. 2022Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Groundingcited by 29
  34. 2019Relationship-Embedded Representation Learning for Grounding Referring Expressionscited by 25
  35. 2022Improving Visual Grounding by Encouraging Consistent Gradient-based Explanationscited by 24
  36. 2021Word2Pix: Word to Pixel Cross Attention Transformer in Visual Groundingcited by 16
  37. 2023Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognitioncited by 16
  38. 2019Variational Context: Exploiting Visual and Textual Context for Grounding Referring Expressionscited by 12

50 most cited of 52 All 52 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Visual Position Prompt for MLLM based Visual Groundingcited by 16
  2. 2026Plug-and-Adapt: Multimodal Coreference Resolution at First Sight with a Pretrained Alignment Modelcited by 0