---
title: "Continuous Attentive Multimodal Prompt Tuning for Few-Shot"
description: "The authors propose CAMP, which recasts classification as filling a [MASK] slot in a… 0.729 accuracy. 64 references, cited by 1 paper here."
authors: ["Soumyadeep Jana", "Animesh Dey", "Ranbir Singh Sanasam"]
year: 2024
venue: "Proceedings of the 28th Conference on Computational Natural Language Learning"
source: "https://aclanthology.org/2024.conll-1.25/"
doi: "10.18653/v1/2024.conll-1.25"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/jana-2024-continuous-attentive-multimodal-prompt-tuning/"
---
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# Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection

**Authors:** Soumyadeep Jana, Animesh Dey and [Ranbir Singh Sanasam](https://md.research.elamur.ai/free/en/authors/ranbir-singh-sanasam/) · **Year:** 2024 · **Venue:** [Proceedings of the 28th Conference on Computational Natural Language Learning](https://md.research.elamur.ai/free/en/venues/conll/) ([CoNLL 2024](https://md.research.elamur.ai/free/en/venues/conll/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.conll-1.25/) · [PDF](https://aclanthology.org/2024.conll-1.25.pdf) · [DOI 10.18653/v1/2024.conll-1.25](https://doi.org/10.18653/v1/2024.conll-1.25) · [OpenAlex](https://openalex.org/W4404783884)

**Citations:** 4 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper addresses detecting sarcasm in image–text social media posts when only a few labelled examples are available. Large annotated sarcasm corpora are costly to build, and heavy multimodal models tend to overfit their training domain. The authors propose CAMP, which recasts classification as filling a [MASK] slot in a frozen BERT-base model. Images enter the model in two ways: ResNet features are projected into the text embedding space as pseudo-visual tokens, and BLIP-2 generates a caption that serves as a third, textual modality. Learnable continuous prompt tokens are prepended to the input. A dedicated attention mechanism lets these tokens interact with the image and text tokens, so they are not learned in isolation. Experiments use 1% of the training data of MMSD and MMSD2.0, plus an out-of-distribution test. CAMP beats multimodal baselines in the few-shot and OOD settings while updating only about 30% of the weights. This makes the approach a cheap and robust option for label-scarce figurative-language tasks.

## Key points
- The few-shot setup draws two random 1% splits of the training data and averages the results. The benchmarks are MMSD and MMSD2.0, and MCMD is also used in the evaluation.
- The prompt template is: visual tokens, "Tweet text: [T]", "Caption: [C]", then [MASK]. The captions come from BLIP-2, and the visual tokens are ResNet features with a linear projection.
- CAMP reaches 0.729 accuracy and 0.717 F1 on MMSD, and 0.692 accuracy and 0.681 F1 on MMSD2.0.
- Zero-training prompting with discrete templates is far weaker, at about 0.57–0.60 accuracy. Swapping one discrete template for another shifts accuracy by 2.7% on MMSD and 1.8% on MMSD2.0.
- The attention module adds on average +2.2% accuracy on MMSD and +1.33% on MMSD2.0 across prompt lengths from 10 to 100. Full prompt-based fine-tuning beats CAMP by 1.7% on MMSD and shows no gain on MMSD2.0, at a much higher training cost.

## Interesting ideas
- Turning a non-text modality into a caption gives the language model a shared textual bridge, and removing the caption measurably hurts results.
- Sarcasm is treated as a mismatch between what the text says and what the attached context shows, which is a useful way to frame irony detection.
- The authors compare three regimes on the same templates: pure prompting, prompt-based fine-tuning, and prompt tuning. This separates what the frozen model already knows from what training adds.
- The paper reports how much accuracy moves when only the template wording changes, which exposes fragile setups.
- Sweeping the number of prompt tokens shows gains up to a point and then decline from redundancy and overfitting. Averaging over two random 1% splits reduces sampling luck.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): Image or table inputs could be verbalised into short descriptive sentences, then parsed like ordinary text, so their content joins the world of the text through the same explainable path.
- **I2** (frames): The irony module in prag could flag a speech act whose literal evaluation conflicts with the states recorded for the described situation, and use that contrast as an explainable irony cue.
- **I3** (learn): LLM teacher tiers could be evaluated in the same way, contrasting zero-shot output, lightly adapted output and fully trained students, to show how much each tier contributes.
- **I4** (learn): Paraphrased-instruction variants could serve as a robustness control when querying LLM teachers for silver data, so that labels shifting under rewording get discarded.
- **I5** (learn): Small-data training runs for repair rules or parsers should report several random subsamples and capacity sweeps, so that improvements are not artefacts of one lucky split.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Indian Institute of Technology Guwahati](https://md.research.elamur.ai/free/en/universities/indian-institute-of-technology-guwahati/) — University, Guwahati, India · 3 works · 46 citations

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works

🧭 Topics (2): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 1,035 works · [🖼️ Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/) · 811 works

## Cite

Cite as: Soumyadeep Jana, Animesh Dey and Ranbir Singh Sanasam. 2024. Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection. Proceedings of the 28th Conference on Computational Natural Language Learning.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{jana2024continuous,
  title = {{Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection}},
  author = {Soumyadeep Jana, Animesh Dey and Ranbir Singh Sanasam},
  booktitle = {Proceedings of the 28th Conference on Computational Natural Language Learning},
  year = {2024},
  doi = {10.18653/v1/2024.conll-1.25},
  url = {https://doi.org/10.18653/v1/2024.conll-1.25}
}
```

- [← previous Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languages](https://md.research.elamur.ai/free/en/papers/krasitskii-2025-comparative-approaches-sentiment-analysis-using/)
- [next → DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers](https://md.research.elamur.ai/free/en/papers/skiredj-2024-darijabanking-new-resource-overcoming-language/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/13/)

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64 references, 32 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*