mlsanityData to 7 Oct 2026

Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning

arXiv 2009.14352 ↗First posted 30 Sep 2020cs.CVcited by 26

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Abstract (arXiv)

Change Captioning is a task that aims to describe the difference between images with natural language. Most existing methods treat this problem as a difference judgment without the existence of distractors, such as viewpoint changes. However, in practice, viewpoint changes happen often and can overwhelm the semantic difference to be described. In this paper, we propose a novel visual encoder to explicitly distinguish viewpoint changes from semantic changes in the change captioning task. Moreover, we further simulate the attention preference of humans and propose a novel reinforcement learning process to fine-tune the attention directly with language evaluation rewards. Extensive experimental results show that our method outperforms the state-of-the-art approaches by a large margin in both Spot-the-Diff and CLEVR-Change datasets.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 11; 2025: 2; 2026: 4.

26 papers.

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  1. 2021From Show to Tell: A Survey on Deep Learning-based Image Captioningcited by 137
  2. 2021Describing and Localizing Multiple Changes with Transformerscited by 47
  3. 2023Changes to Captions: An Attentive Network for Remote Sensing Change Captioningcited by 41
  4. 2022Image Difference Captioning with Pre-training and Contrastive Learningcited by 39
  5. 2022CLIP4IDC: CLIP for Image Difference Captioningcited by 25
  6. 2023Self-supervised Cross-view Representation Reconstruction for Change Captioningcited by 20
  7. 2024Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Modelscited by 17
  8. 2023Neighborhood Contrastive Transformer for Change Captioningcited by 16
  9. 2024Enhancing Perception of Key Changes in Remote Sensing Image Change Captioningcited by 16
  10. 2024OneDiff: A Generalist Model for Image Difference Captioningcited by 15
  11. 2021R3Net:Relation-embedded Representation Reconstruction Network for Change Captioningcited by 12
  12. 2024VIXEN: Visual Text Comparison Network for Image Difference Captioningcited by 12
14 more
  1. 2025OmniDiff: A Comprehensive Benchmark for Fine-grained Image Difference Captioningcited by 12
  2. 2024Context-aware Difference Distilling for Multi-change Captioningcited by 10
  3. 2024Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioningcited by 6
  4. 2024Finetuning CLIP to Reason about Pairwise Differencescited by 6
  5. 2023Viewpoint Integration and Registration with Vision Language Foundation Model for Image Change Understandingcited by 3
  6. 2024MV-CC: Mask Enhanced Video Model for Remote Sensing Change Captioncited by 3
  7. 2024Reframing Image Difference Captioning with BLIP2IDC and Synthetic Augmentationcited by 3
  8. 2024Diffusion-RSCC: Diffusion Probabilistic Model for Change Captioning in Remote Sensing Imagescited by 2
  9. 2025Image Difference Grounding with Natural Languagecited by 1
  10. 2026Imagine How To Change: Explicit Procedure Modeling for Change Captioningcited by 1
  11. 2024Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysiscited by 0
  12. 2026Hierarchical Dual-Change Collaborative Learning for UAV Scene Change Captioningcited by 0
  13. 2026C3-Bench: A Context-Aware Change Captioning Benchmarkcited by 0
  14. 2026Different Changes Require Different Reasoning: Change-Type-Specialized Experts for Robust Change Captioningcited by 0