Papers by Artemis Panagopoulou
I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors (2023.findings-acl)
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Tuhin Chakrabarty, Arkadiy Saakyan, Olivia Winn, Artemis Panagopoulou, Yue Yang, Marianna Apidianaki, Smaranda Muresan
| Challenge: | Visual metaphors are powerful rhetorical devices used to communicate creative ideas through images. |
| Approach: | They propose to generate visual metaphors from linguistic metaphors by using large language models and Diffusion models. |
| Outcome: | The proposed task requires the ability to model implicit meaning and compositionality. |
Visual Goal-Step Inference using wikiHow (2021.emnlp-main)
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| Challenge: | Past work in NLP examined the task of goal-step inference for textual goals . wikiHow dataset shows that goal-step inference is challenging for state-of-the-art models . |
| Approach: | They propose a task where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal. |
| Outcome: | The proposed task is challenging for state-of-the-art multimodal models and can be transferred to other datasets. |
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction (2022.findings-emnlp)
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| Challenge: | Neural language models encode rich knowledge about entities and their relationships but common properties of nouns are difficult to extract because they are rarely explicitly stated in texts. |
| Approach: | They propose to extract perceptual properties from images and use them in an ensemble model to complement the information extracted from language models. |
| Outcome: | The proposed model improves noun property prediction compared to powerful text-based language models. |
Contra4: Evaluating Contrastive Cross-Modal Reasoning in Audio, Video, Image, and 3D (2025.emnlp-main)
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Artemis Panagopoulou, Le Xue, Honglu Zhou, Silvio Savarese, Ran Xu, Caiming Xiong, Chris Callison-Burch, Mark Yatskar, Juan Carlos Niebles
| Challenge: | a recent study shows that multimodal models can reason across multiple modalities . a limited number of models are able to reason across a variety of inputs . |
| Approach: | They propose a dataset for contrastive cross-modal reasoning across four modalities . they use human annotations and a mixture-of-models round-trip-consistency filter . |
| Outcome: | a new model evaluates models on multiple modalities to determine which one best answers a natural language prompt . the model must select the one that best satisfies the query and then fine-tune it . state-of-the-art models still achieve only 56% accuracy overall and 42% in four-modal settings . |