Papers with multimodal applications
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Multimodal large language models combine visual and textual data for tasks like image captioning and visual question answering. |
| Approach: | They propose temperature scaling and iterative prompt optimization to calibrate MLLMs and enhance model reliability. |
| Outcome: | The proposed techniques improve MLLMs and improve model reliability. |
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response (2024.findings-naacl)
Copied to clipboard
Zihao Deng, Yinghao Ma, Yudong Liu, Rongchen Guo, Ge Zhang, Wenhu Chen, Wenhao Huang, Emmanouil Benetos
| Challenge: | Large Language Models have shown immense potential in multimodal applications, but convergence between textual and musical domains remains unexplored. |
| Approach: | They propose a system that aligns music representations with a frozen LLM . they train the system on an extensive music caption dataset and fine-tune it with instructional data . |
| Outcome: | The proposed system bridges the gap between music audio and textual contexts by combining music captions with a frozen model . it performs well in generating music caption and composing music-related Q&A pairs . the proposed system is available for free download at http://www.musilingo.com/ . |
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types (2022.acl-long)
Copied to clipboard
| Challenge: | Multimodal Entity Linking (MEL) is an essential task for many multimodal applications. |
| Approach: | They propose to use a human-annotated Wikipedia-based multimodal entity linking dataset to improve the quality of existing MEL models. |
| Outcome: | The proposed model uses the visual information of images more effectively than existing models. |
MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing multimodal classification systems use tabular, textual, and visual data to provide efficient and scalable services. |
| Approach: | They propose a multimodal classification benchmark MuG with eight datasets . they analyze label balance ratios, percentages of missing features, distributions of data within each modality . |
| Outcome: | The proposed benchmark is available on https://github.com/lujiaying/MUG-Bench . it includes eight datasets that allow researchers to evaluate and improve their models . |
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in Vision-Language Models (VLMs) have broadened the scope of multimodal applications, but evaluations often neglect abstract dimensions such as personality traits and human values. |
| Approach: | They propose a Visual Question Answering (VQA) benchmark based on Schwartz’s value dimensions that capture core human values guiding people’s preferences and actions. |
| Outcome: | The proposed model can be used to evaluate visual question answering (VQA) tasks and to simulate diverse personas. |
Aligning Images and Text with Semantic Role Labels for Fine-Grained Cross-Modal Understanding (2022.lrec-1)
Copied to clipboard
| Challenge: | Currently, image retrieval systems can retrieve relevant results for diverse inputs, but they do not provide a way to intentionally inject variety into the search results. |
| Approach: | They propose a multimodal dataset that combines semantic annotations with image bounding boxes. |
| Outcome: | The proposed system improves image retrieval performance and flexibility. |