# Positive-Augmented Contrastive Learning for Image and Video Captioning Evaluation

arXiv: https://arxiv.org/abs/2303.12112 · HTML: https://mlsanity.com/paper/2303.12112/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 75 references that are in the map.
- First posted on arXiv 21 Mar 2023.

## Builds on (most cited first, 30 of 53)

- [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)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [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)
- [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)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors](https://mlsanity.com/paper/2203.13131/) (2022)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research](https://mlsanity.com/paper/1904.03493/) (2019)
- [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)

## Built on by (most cited first, 30 of 78)

- [Benchmarking and Improving Detail Image Caption](https://mlsanity.com/paper/2405.19092/) (2024)
- [Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs](https://mlsanity.com/paper/2404.15406/) (2024)
- [Polos: Multimodal Metric Learning from Human Feedback for Image Captioning](https://mlsanity.com/paper/2402.18091/) (2024)
- [FLEUR: An Explainable Reference-Free Evaluation Metric for Image Captioning Using a Large Multimodal Model](https://mlsanity.com/paper/2406.06004/) (2024)
- [Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images](https://mlsanity.com/paper/2304.00500/) (2023)
- [Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning](https://mlsanity.com/paper/2503.07906/) (2025)
- [G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o](https://mlsanity.com/paper/2412.13647/) (2024)
- [Mitigating Open-Vocabulary Caption Hallucinations](https://mlsanity.com/paper/2312.03631/) (2023)
- [With a Little Help from your own Past: Prototypical Memory Networks for Image Captioning](https://mlsanity.com/paper/2308.12383/) (2023)
- [Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives](https://mlsanity.com/paper/2503.14604/) (2025)
- [Positive-Augmented Contrastive Learning for Vision-and-Language Evaluation and Training](https://mlsanity.com/paper/2410.07336/) (2024)
- [SC-Captioner: Improving Image Captioning with Self-Correction by Reinforcement Learning](https://mlsanity.com/paper/2508.06125/) (2025)
- [BRIDGE: Bridging Gaps in Image Captioning Evaluation with Stronger Visual Cues](https://mlsanity.com/paper/2407.20341/) (2024)
- [HICEScore: A Hierarchical Metric for Image Captioning Evaluation](https://mlsanity.com/paper/2407.18589/) (2024)
- [An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics](https://mlsanity.com/paper/2305.14998/) (2023)
- [DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2409.19255/) (2024)
- [Pretrained Image-Text Models are Secretly Video Captioners](https://mlsanity.com/paper/2502.13363/) (2025)
- [I0T: Embedding Standardization Method Towards Zero Modality Gap](https://mlsanity.com/paper/2412.14384/) (2024)
- [4D-Bench: Benchmarking Multi-modal Large Language Models for 4D Object Understanding](https://mlsanity.com/paper/2503.17827/) (2025)
- [Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey](https://mlsanity.com/paper/2307.16236/) (2023)
- [Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction](https://mlsanity.com/paper/2402.17969/) (2024)
- [Towards Retrieval-Augmented Architectures for Image Captioning](https://mlsanity.com/paper/2405.13127/) (2024)
- [Efficient and Versatile Robust Fine-Tuning of Zero-shot Models](https://mlsanity.com/paper/2408.05749/) (2024)
- [EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations](https://mlsanity.com/paper/2506.24016/) (2025)
- [ExGra-Med: Extended Context Graph Alignment for Medical Vision-Language Models](https://mlsanity.com/paper/2410.02615/) (2024)
- [Panoptic Captioning: An Equivalence Bridge for Image and Text](https://mlsanity.com/paper/2505.16334/) (2025)
- [SignVTCL: Multi-Modal Continuous Sign Language Recognition Enhanced by Visual-Textual Contrastive Learning](https://mlsanity.com/paper/2401.11847/) (2024)
- [Revisiting Image Captioning Training Paradigm via Direct CLIP-based Optimization](https://mlsanity.com/paper/2408.14547/) (2024)
- [Audio Description Generation in the Era of LLMs and VLMs: A Review of Transferable Generative AI Technologies](https://mlsanity.com/paper/2410.08860/) (2024)
- [Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted Captions](https://mlsanity.com/paper/2411.09018/) (2024)

## Abstract (arXiv)

The CLIP model has been recently proven to be very effective for a variety of cross-modal tasks, including the evaluation of captions generated from vision-and-language architectures. In this paper, we propose a new recipe for a contrastive-based evaluation metric for image captioning, namely Positive-Augmented Contrastive learning Score (PAC-S), that in a novel way unifies the learning of a contrastive visual-semantic space with the addition of generated images and text on curated data. Experiments spanning several datasets demonstrate that our new metric achieves the highest correlation with human judgments on both images and videos, outperforming existing reference-based metrics like CIDEr and SPICE and reference-free metrics like CLIP-Score. Finally, we test the system-level correlation of the proposed metric when considering popular image captioning approaches, and assess the impact of employing different cross-modal features. Our source code and trained models are publicly available at: https://github.com/aimagelab/pacscore.