# Let there be a clock on the beach: Reducing Object Hallucination in Image Captioning

arXiv: https://arxiv.org/abs/2110.01705 · HTML: https://mlsanity.com/paper/2110.01705/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 48 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [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)
- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [Women also Snowboard: Overcoming Bias in Captioning Models](https://mlsanity.com/paper/1803.09797/) (2018)
- [Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing](https://mlsanity.com/paper/1912.07538/) (2019)
- [Neural Baby Talk](https://mlsanity.com/paper/1803.09845/) (2018)
- [Visual Commonsense R-CNN](https://mlsanity.com/paper/2002.12204/) (2020)
- [Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs](https://mlsanity.com/paper/2003.00387/) (2020)
- [Good News, Everyone! Context driven entity-aware captioning for news images](https://mlsanity.com/paper/1904.01475/) (2019)
- [Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions](https://mlsanity.com/paper/1811.10652/) (2018)
- [Deconfounded Image Captioning: A Causal Retrospect](https://mlsanity.com/paper/2003.03923/) (2020)
- [Transform and Tell: Entity-Aware News Image Captioning](https://mlsanity.com/paper/2004.08070/) (2020)
- [Comprehensive Image Captioning via Scene Graph Decomposition](https://mlsanity.com/paper/2007.11731/) (2020)
- [Captioning Images with Diverse Objects](https://mlsanity.com/paper/1606.07770/) (2016)

## Built on by (most cited first, 30 of 76)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Aligning Large Multimodal Models with Factually Augmented RLHF](https://mlsanity.com/paper/2309.14525/) (2023)
- [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2311.16922/) (2023)
- [Analyzing and Mitigating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2310.00754/) (2023)
- [HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding](https://mlsanity.com/paper/2403.00425/) (2024)
- [Evaluation and Analysis of Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2308.15126/) (2023)
- [Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision](https://mlsanity.com/paper/2311.07362/) (2023)
- [Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models](https://mlsanity.com/paper/2408.02032/) (2024)
- [Cognitive Mirage: A Review of Hallucinations in Large Language Models](https://mlsanity.com/paper/2309.06794/) (2023)
- [Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models](https://mlsanity.com/paper/2310.05338/) (2023)
- [Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention](https://mlsanity.com/paper/2406.12718/) (2024)
- [Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training](https://mlsanity.com/paper/2210.07688/) (2022)
- [Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites](https://mlsanity.com/paper/2312.01701/) (2023)
- [The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio](https://mlsanity.com/paper/2410.12787/) (2024)
- [Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance](https://mlsanity.com/paper/2402.08680/) (2024)
- [Nullu: Mitigating Object Hallucinations in Large Vision-Language Models via HalluSpace Projection](https://mlsanity.com/paper/2412.13817/) (2024)
- [UnIVAL: Unified Model for Image, Video, Audio and Language Tasks](https://mlsanity.com/paper/2307.16184/) (2023)
- [ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large language Models](https://mlsanity.com/paper/2503.13107/) (2025)
- [AI Hallucinations: A Misnomer Worth Clarifying](https://mlsanity.com/paper/2401.06796/) (2024)
- [Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling](https://mlsanity.com/paper/2504.13169/) (2025)
- [HallE-Control: Controlling Object Hallucination in Large Multimodal Models](https://mlsanity.com/paper/2310.01779/) (2023)
- [CAFe: Unifying Representation and Generation with Contrastive-Autoregressive Finetuning](https://mlsanity.com/paper/2503.19900/) (2025)
- [Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning](https://mlsanity.com/paper/2310.00647/) (2023)
- [AVCD: Mitigating Hallucinations in Audio-Visual Large Language Models through Contrastive Decoding](https://mlsanity.com/paper/2505.20862/) (2025)
- [Implicit Multimodal Alignment: On the Generalization of Frozen LLMs to Multimodal Inputs](https://mlsanity.com/paper/2405.16700/) (2024)
- [MASH-VLM: Mitigating Action-Scene Hallucination in Video-LLMs through Disentangled Spatial-Temporal Representations](https://mlsanity.com/paper/2503.15871/) (2025)
- [Zero-Resource Hallucination Prevention for Large Language Models](https://mlsanity.com/paper/2309.02654/) (2023)
- [Mitigating Open-Vocabulary Caption Hallucinations](https://mlsanity.com/paper/2312.03631/) (2023)
- [Towards Rationality in Language and Multimodal Agents: A Survey](https://mlsanity.com/paper/2406.00252/) (2024)

## Abstract (arXiv)

Explaining an image with missing or non-existent objects is known as object bias (hallucination) in image captioning. This behaviour is quite common in the state-of-the-art captioning models which is not desirable by humans. To decrease the object hallucination in captioning, we propose three simple yet efficient training augmentation method for sentences which requires no new training data or increase in the model size. By extensive analysis, we show that the proposed methods can significantly diminish our models' object bias on hallucination metrics. Moreover, we experimentally demonstrate that our methods decrease the dependency on the visual features. All of our code, configuration files and model weights will be made public.