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

arXiv: https://arxiv.org/abs/2009.14352 · HTML: https://mlsanity.com/paper/2009.14352/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 26 of 26)

- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [Describing and Localizing Multiple Changes with Transformers](https://mlsanity.com/paper/2103.14146/) (2021)
- [Changes to Captions: An Attentive Network for Remote Sensing Change Captioning](https://mlsanity.com/paper/2304.01091/) (2023)
- [Image Difference Captioning with Pre-training and Contrastive Learning](https://mlsanity.com/paper/2202.04298/) (2022)
- [CLIP4IDC: CLIP for Image Difference Captioning](https://mlsanity.com/paper/2206.00629/) (2022)
- [Self-supervised Cross-view Representation Reconstruction for Change Captioning](https://mlsanity.com/paper/2309.16283/) (2023)
- [Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models](https://mlsanity.com/paper/2408.04594/) (2024)
- [Neighborhood Contrastive Transformer for Change Captioning](https://mlsanity.com/paper/2303.03171/) (2023)
- [Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2409.12612/) (2024)
- [OneDiff: A Generalist Model for Image Difference Captioning](https://mlsanity.com/paper/2407.05645/) (2024)
- [R3Net:Relation-embedded Representation Reconstruction Network for Change Captioning](https://mlsanity.com/paper/2110.10328/) (2021)
- [VIXEN: Visual Text Comparison Network for Image Difference Captioning](https://mlsanity.com/paper/2402.19119/) (2024)
- [OmniDiff: A Comprehensive Benchmark for Fine-grained Image Difference Captioning](https://mlsanity.com/paper/2503.11093/) (2025)
- [Context-aware Difference Distilling for Multi-change Captioning](https://mlsanity.com/paper/2405.20810/) (2024)
- [Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning](https://mlsanity.com/paper/2407.11683/) (2024)
- [Finetuning CLIP to Reason about Pairwise Differences](https://mlsanity.com/paper/2409.09721/) (2024)
- [Viewpoint Integration and Registration with Vision Language Foundation Model for Image Change Understanding](https://mlsanity.com/paper/2309.08585/) (2023)
- [MV-CC: Mask Enhanced Video Model for Remote Sensing Change Caption](https://mlsanity.com/paper/2410.23946/) (2024)
- [Reframing Image Difference Captioning with BLIP2IDC and Synthetic Augmentation](https://mlsanity.com/paper/2412.15939/) (2024)
- [Diffusion-RSCC: Diffusion Probabilistic Model for Change Captioning in Remote Sensing Images](https://mlsanity.com/paper/2405.12875/) (2024)
- [Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis](https://mlsanity.com/paper/2408.04909/) (2024)
- [Image Difference Grounding with Natural Language](https://mlsanity.com/paper/2504.01952/) (2025)
- [Imagine How To Change: Explicit Procedure Modeling for Change Captioning](https://mlsanity.com/paper/2603.05969/) (2026)
- [Hierarchical Dual-Change Collaborative Learning for UAV Scene Change Captioning](https://mlsanity.com/paper/2603.12832/) (2026)
- [C3-Bench: A Context-Aware Change Captioning Benchmark](https://mlsanity.com/paper/2606.25445/) (2026)
- [Different Changes Require Different Reasoning: Change-Type-Specialized Experts for Robust Change Captioning](https://mlsanity.com/paper/2609.01136/) (2026)

## 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.