# Transferable Decoding with Visual Entities for Zero-Shot Image Captioning

arXiv: https://arxiv.org/abs/2307.16525 · HTML: https://mlsanity.com/paper/2307.16525/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 57 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 30 of 45)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [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)
- [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://mlsanity.com/paper/2102.08981/) (2021)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://mlsanity.com/paper/2202.11094/) (2022)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [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)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [Image Captioning with Semantic Attention](https://mlsanity.com/paper/1603.03925/) (2016)

## Built on by (most cited first, 30 of 34)

- [Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks](https://mlsanity.com/paper/2411.15720/) (2024)
- [MeaCap: Memory-Augmented Zero-shot Image Captioning](https://mlsanity.com/paper/2403.03715/) (2024)
- [EVCap: Retrieval-Augmented Image Captioning with External Visual-Name Memory for Open-World Comprehension](https://mlsanity.com/paper/2311.15879/) (2023)
- [Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models](https://mlsanity.com/paper/2308.11186/) (2023)
- [As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?](https://mlsanity.com/paper/2403.12693/) (2024)
- [IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot Captioning](https://mlsanity.com/paper/2409.18046/) (2024)
- [Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning](https://mlsanity.com/paper/2501.00437/) (2024)
- [Scale Efficient Training for Large Datasets](https://mlsanity.com/paper/2503.13385/) (2025)
- [Unconstrained Open Vocabulary Image Classification: Zero-Shot Transfer from Text to Image via CLIP Inversion](https://mlsanity.com/paper/2407.11211/) (2024)
- [ANNEXE: Unified Analyzing, Answering, and Pixel Grounding for Egocentric Interaction](https://mlsanity.com/paper/2504.01472/) (2025)
- [HoPE: Hybrid of Position Embedding for Long Context Vision-Language Models](https://mlsanity.com/paper/2505.20444/) (2025)
- [Controllable Contextualized Image Captioning: Directing the Visual Narrative through User-Defined Highlights](https://mlsanity.com/paper/2407.11449/) (2024)
- [Visual Zero-Shot E-Commerce Product Attribute Value Extraction](https://mlsanity.com/paper/2502.15979/) (2025)
- [SynC: Synthetic Image Caption Dataset Refinement with One-to-many Mapping for Zero-shot Image Captioning](https://mlsanity.com/paper/2507.18616/) (2025)
- [One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework](https://mlsanity.com/paper/2510.02898/) (2025)
- [Medusa: Cross-Modal Transferable Adversarial Attacks on Multimodal Medical Retrieval-Augmented Generation](https://mlsanity.com/paper/2511.19257/) (2025)
- [DETER: Detecting Edited Regions for Deterring Generative Manipulations](https://mlsanity.com/paper/2312.10539/) (2023)
- [Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis](https://mlsanity.com/paper/2408.04909/) (2024)
- [Debiasing Vison-Language Models with Text-Only Training](https://mlsanity.com/paper/2410.09365/) (2024)
- [DIR: Retrieval-Augmented Image Captioning with Comprehensive Understanding](https://mlsanity.com/paper/2412.01115/) (2024)
- [Text-Only Training for Image Captioning with Retrieval Augmentation and Modality Gap Correction](https://mlsanity.com/paper/2512.04309/) (2025)
- [How to Understand Named Entities: Using Common Sense for News Captioning](https://mlsanity.com/paper/2403.06520/) (2024)
- [TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning](https://mlsanity.com/paper/2409.19960/) (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)
- [RACap: Relation-Aware Prompting for Lightweight Retrieval-Augmented Image Captioning](https://mlsanity.com/paper/2509.15883/) (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)
- [Caption Generation for Dongba Paintings via Prompt Learning and Semantic Fusion](https://mlsanity.com/paper/2603.22946/) (2026)
- [The Language of Touch: Translating Vibrations into Text with Dual-Branch Learning](https://mlsanity.com/paper/2603.26804/) (2026)
- [Batch Loss Score for Dynamic Data Pruning](https://mlsanity.com/paper/2604.04681/) (2026)

## Abstract (arXiv)

Image-to-text generation aims to describe images using natural language. Recently, zero-shot image captioning based on pre-trained vision-language models (VLMs) and large language models (LLMs) has made significant progress. However, we have observed and empirically demonstrated that these methods are susceptible to modality bias induced by LLMs and tend to generate descriptions containing objects (entities) that do not actually exist in the image but frequently appear during training (i.e., object hallucination). In this paper, we propose ViECap, a transferable decoding model that leverages entity-aware decoding to generate descriptions in both seen and unseen scenarios. ViECap incorporates entity-aware hard prompts to guide LLMs' attention toward the visual entities present in the image, enabling coherent caption generation across diverse scenes. With entity-aware hard prompts, ViECap is capable of maintaining performance when transferring from in-domain to out-of-domain scenarios. Extensive experiments demonstrate that ViECap sets a new state-of-the-art cross-domain (transferable) captioning and performs competitively in-domain captioning compared to previous VLMs-based zero-shot methods. Our code is available at: https://github.com/FeiElysia/ViECap