# Zero-Shot Video Captioning with Evolving Pseudo-Tokens

arXiv: https://arxiv.org/abs/2207.11100 · HTML: https://mlsanity.com/paper/2207.11100/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 212 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 30 of 52)

- [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)
- [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)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (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)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Universal Adversarial Triggers for Attacking and Analyzing NLP](https://mlsanity.com/paper/1908.07125/) (2019)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding](https://mlsanity.com/paper/2109.14084/) (2021)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text](https://mlsanity.com/paper/2104.11178/) (2021)
- [Multi-modal Transformer for Video Retrieval](https://mlsanity.com/paper/2007.10639/) (2020)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [A simple neural network module for relational reasoning](https://mlsanity.com/paper/1706.01427/) (2017)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)

## Built on by (most cited first, 25 of 25)

- [Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?](https://mlsanity.com/paper/2301.00184/) (2022)
- [DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training](https://mlsanity.com/paper/2303.03032/) (2023)
- [AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder](https://mlsanity.com/paper/2306.06370/) (2023)
- [MeaCap: Memory-Augmented Zero-shot Image Captioning](https://mlsanity.com/paper/2403.03715/) (2024)
- [VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending](https://mlsanity.com/paper/2305.13167/) (2023)
- [MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning](https://mlsanity.com/paper/2308.13218/) (2023)
- [From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mapping](https://mlsanity.com/paper/2304.13273/) (2023)
- [Zero-shot audio captioning with audio-language model guidance and audio context keywords](https://mlsanity.com/paper/2311.08396/) (2023)
- [IFCap: Image-like Retrieval and Frequency-based Entity Filtering for Zero-shot Captioning](https://mlsanity.com/paper/2409.18046/) (2024)
- [DisCLIP: Open-Vocabulary Referring Expression Generation](https://mlsanity.com/paper/2305.19108/) (2023)
- [Fine-Grained Captioning of Long Videos through Scene Graph Consolidation](https://mlsanity.com/paper/2502.16427/) (2025)
- [RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning](https://mlsanity.com/paper/2405.07046/) (2024)
- [ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation](https://mlsanity.com/paper/2303.06458/) (2023)
- [Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment](https://mlsanity.com/paper/2307.02682/) (2023)
- [ReSeTOX: Re-learning attention weights for toxicity mitigation in machine translation](https://mlsanity.com/paper/2305.11761/) (2023)
- [Zero-Shot Audio Captioning via Audibility Guidance](https://mlsanity.com/paper/2309.03884/) (2023)
- [Zero-shot Translation of Attention Patterns in VQA Models to Natural Language](https://mlsanity.com/paper/2311.05043/) (2023)
- [SnapCap: Efficient Snapshot Compressive Video Captioning](https://mlsanity.com/paper/2401.04903/) (2024)
- [Describing Sets of Images with Textual-PCA](https://mlsanity.com/paper/2210.12112/) (2022)
- [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)
- [LogSTOP: Temporal Scores over Prediction Sequences for Matching and Retrieval](https://mlsanity.com/paper/2510.06512/) (2025)
- [MobiDiary: Autoregressive Action Captioning with Wearable Devices and Wireless Signals](https://mlsanity.com/paper/2601.08204/) (2026)
- [ConTrans: Learning Text-enhanced Local-global Temporal Representations for Zero-shot Temporal Action Localization](https://mlsanity.com/paper/2605.30689/) (2026)
- [Watching Synthetic Videos: Aligning Cross-modal Representations with Visual Synthesis for Zero-shot Video Captioning](https://mlsanity.com/paper/2608.11013/) (2026)

## Abstract (arXiv)

We introduce a zero-shot video captioning method that employs two frozen networks: the GPT-2 language model and the CLIP image-text matching model. The matching score is used to steer the language model toward generating a sentence that has a high average matching score to a subset of the video frames. Unlike zero-shot image captioning methods, our work considers the entire sentence at once. This is achieved by optimizing, during the generation process, part of the prompt from scratch, by modifying the representation of all other tokens in the prompt, and by repeating the process iteratively, gradually improving the specificity and comprehensiveness of the generated sentence. Our experiments show that the generated captions are coherent and display a broad range of real-world knowledge. Our code is available at: https://github.com/YoadTew/zero-shot-video-to-text