# Reflective Decoding Network for Image Captioning

arXiv: https://arxiv.org/abs/1908.11824 · HTML: https://mlsanity.com/paper/1908.11824/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 30 of 31)

- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models](https://mlsanity.com/paper/2504.15279/) (2025)
- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [GRIT: Faster and Better Image captioning Transformer Using Dual Visual Features](https://mlsanity.com/paper/2207.09666/) (2022)
- [CPTR: Full Transformer Network for Image Captioning](https://mlsanity.com/paper/2101.10804/) (2021)
- [eP-ALM: Efficient Perceptual Augmentation of Language Models](https://mlsanity.com/paper/2303.11403/) (2023)
- [Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning](https://mlsanity.com/paper/2205.04363/) (2022)
- [Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices](https://mlsanity.com/paper/2503.06063/) (2025)
- [Efficient Vision-Language Pretraining with Visual Concepts and Hierarchical Alignment](https://mlsanity.com/paper/2208.13628/) (2022)
- [Ultrasound Report Generation with Cross-Modality Feature Alignment via Unsupervised Guidance](https://mlsanity.com/paper/2406.00644/) (2024)
- [GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing](https://mlsanity.com/paper/2412.02366/) (2024)
- [Image Captioning in news report scenario](https://mlsanity.com/paper/2403.16209/) (2024)
- [Is this Generated Person Existed in Real-world? Fine-grained Detecting and Calibrating Abnormal Human-body](https://mlsanity.com/paper/2411.14205/) (2024)
- [Neural Attention for Image Captioning: Review of Outstanding Methods](https://mlsanity.com/paper/2111.15015/) (2021)
- [Sensor-Augmented Egocentric-Video Captioning with Dynamic Modal Attention](https://mlsanity.com/paper/2109.02955/) (2021)
- [Album Storytelling with Iterative Story-aware Captioning and Large Language Models](https://mlsanity.com/paper/2305.12943/) (2023)
- [ClipSitu: Effectively Leveraging CLIP for Conditional Predictions in Situation Recognition](https://mlsanity.com/paper/2307.00586/) (2023)
- [MMGenBench: Fully Automatically Evaluating LMMs from the Text-to-Image Generation Perspective](https://mlsanity.com/paper/2411.14062/) (2024)
- [Image Captioning In the Transformer Age](https://mlsanity.com/paper/2204.07374/) (2022)
- [BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding](https://mlsanity.com/paper/2409.07889/) (2024)
- [Detailed Object Description with Controllable Dimensions](https://mlsanity.com/paper/2411.19106/) (2024)
- [Guiding Attention using Partial-Order Relationships for Image Captioning](https://mlsanity.com/paper/2204.07476/) (2022)
- [Are metrics measuring what they should? An evaluation of image captioning task metrics](https://mlsanity.com/paper/2207.01733/) (2022)
- [A request for clarity over the End of Sequence token in the Self-Critical Sequence Training](https://mlsanity.com/paper/2305.12254/) (2023)
- [Rethinking Sparse Lexical Representations for Image Retrieval in the Age of Rising Multi-Modal Large Language Models](https://mlsanity.com/paper/2408.16296/) (2024)
- [Latent Beam Diffusion Models for Generating Visual Sequences](https://mlsanity.com/paper/2503.20429/) (2025)
- [Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis](https://mlsanity.com/paper/2408.04909/) (2024)
- [Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation](https://mlsanity.com/paper/2506.05399/) (2025)
- [Image Captioning based on Feature Refinement and Reflective Decoding](https://mlsanity.com/paper/2206.07986/) (2022)
- [An Image captioning algorithm based on the Hybrid Deep Learning Technique (CNN+GRU)](https://mlsanity.com/paper/2301.02440/) (2023)

## Abstract (arXiv)

State-of-the-art image captioning methods mostly focus on improving visual features, less attention has been paid to utilizing the inherent properties of language to boost captioning performance. In this paper, we show that vocabulary coherence between words and syntactic paradigm of sentences are also important to generate high-quality image caption. Following the conventional encoder-decoder framework, we propose the Reflective Decoding Network (RDN) for image captioning, which enhances both the long-sequence dependency and position perception of words in a caption decoder. Our model learns to collaboratively attend on both visual and textual features and meanwhile perceive each word's relative position in the sentence to maximize the information delivered in the generated caption. We evaluate the effectiveness of our RDN on the COCO image captioning datasets and achieve superior performance over the previous methods. Further experiments reveal that our approach is particularly advantageous for hard cases with complex scenes to describe by captions.