# MeaCap: Memory-Augmented Zero-shot Image Captioning

arXiv: https://arxiv.org/abs/2403.03715 · HTML: https://mlsanity.com/paper/2403.03715/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 69 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 30 of 58)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [nocaps: novel object captioning at scale](https://mlsanity.com/paper/1812.08658/) (2018)
- [Meshed-Memory Transformer for Image Captioning](https://mlsanity.com/paper/1912.08226/) (2019)

## Built on by (most cited first, 30 of 35)

- [Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation](https://mlsanity.com/paper/2408.00555/) (2024)
- [FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity](https://mlsanity.com/paper/2411.15411/) (2024)
- [Historical Test-time Prompt Tuning for Vision Foundation Models](https://mlsanity.com/paper/2410.20346/) (2024)
- [HICEScore: A Hierarchical Metric for Image Captioning Evaluation](https://mlsanity.com/paper/2407.18589/) (2024)
- [IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot Captioning](https://mlsanity.com/paper/2409.18046/) (2024)
- [RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning](https://mlsanity.com/paper/2405.07046/) (2024)
- [LaVCa: LLM-assisted Visual Cortex Captioning](https://mlsanity.com/paper/2502.13606/) (2025)
- [VL4Gaze: Unleashing Vision-Language Models for Gaze Following](https://mlsanity.com/paper/2512.20735/) (2025)
- [Explaining Domain Shifts in Language: Concept erasing for Interpretable Image Classification](https://mlsanity.com/paper/2503.18483/) (2025)
- [One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework](https://mlsanity.com/paper/2510.02898/) (2025)
- [LLM-Free Image Captioning Evaluation in Reference-Flexible Settings](https://mlsanity.com/paper/2512.21582/) (2025)
- [Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis](https://mlsanity.com/paper/2408.04909/) (2024)
- [DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding](https://mlsanity.com/paper/2412.01115/) (2024)
- [DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement](https://mlsanity.com/paper/2506.15583/) (2025)
- [Text-Only Training for Image Captioning with Retrieval Augmentation and Modality Gap Correction](https://mlsanity.com/paper/2512.04309/) (2025)
- [Leveraging Data to Say No: Memory Augmented Plug-and-Play Selective Prediction](https://mlsanity.com/paper/2601.22570/) (2026)
- [Rigel: Self-Distilled Score Adaptation for Image and Video Captioning Evaluation](https://mlsanity.com/paper/2606.29997/) (2026)
- [How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey](https://mlsanity.com/paper/2412.08158/) (2024)
- [The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning](https://mlsanity.com/paper/2503.23679/) (2025)
- [Zero-Shot, But at What Cost? Unveiling the Hidden Overhead of MILS's LLM-CLIP Framework for Image Captioning](https://mlsanity.com/paper/2504.15199/) (2025)
- [CONCAP: Seeing Beyond English with Concepts Retrieval-Augmented Captioning](https://mlsanity.com/paper/2507.20411/) (2025)
- [ReCap: Event-Aware Image Captioning with Article Retrieval and Semantic Gaussian Normalization](https://mlsanity.com/paper/2509.01259/) (2025)
- [RACap: Relation-Aware Prompting for Lightweight Retrieval-Augmented Image Captioning](https://mlsanity.com/paper/2509.15883/) (2025)
- [DualCap: Enhancing Lightweight Image Captioning via Dual Retrieval with Similar Scenes Visual Prompts](https://mlsanity.com/paper/2510.24813/) (2025)
- [Hyperdimensional Cross-Modal Alignment of Frozen Language and Image Models for Efficient Image Captioning](https://mlsanity.com/paper/2602.23588/) (2026)
- [Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning](https://mlsanity.com/paper/2603.01696/) (2026)
- [Towards Highly Transferable Vision-Language Attack via Semantic-Augmented Dynamic Contrastive Interaction](https://mlsanity.com/paper/2603.04839/) (2026)
- [Caption Generation for Dongba Paintings via Prompt Learning and Semantic Fusion](https://mlsanity.com/paper/2603.22946/) (2026)
- [The Geometry of Compromise: Unlocking Generative Capabilities via Controllable Modality Alignment](https://mlsanity.com/paper/2604.00279/) (2026)
- [Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction](https://mlsanity.com/paper/2606.10571/) (2026)

## Abstract (arXiv)

Zero-shot image captioning (IC) without well-paired image-text data can be divided into two categories, training-free and text-only-training. Generally, these two types of methods realize zero-shot IC by integrating pretrained vision-language models like CLIP for image-text similarity evaluation and a pre-trained language model (LM) for caption generation. The main difference between them is whether using a textual corpus to train the LM. Though achieving attractive performance w.r.t. some metrics, existing methods often exhibit some common drawbacks. Training-free methods tend to produce hallucinations, while text-only-training often lose generalization capability. To move forward, in this paper, we propose a novel Memory-Augmented zero-shot image Captioning framework (MeaCap). Specifically, equipped with a textual memory, we introduce a retrieve-then-filter module to get key concepts that are highly related to the image. By deploying our proposed memory-augmented visual-related fusion score in a keywords-to-sentence LM, MeaCap can generate concept-centered captions that keep high consistency with the image with fewer hallucinations and more world-knowledge. The framework of MeaCap achieves the state-of-the-art performance on a series of zero-shot IC settings. Our code is available at https://github.com/joeyz0z/MeaCap.