Papers with Large multimodal models
Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision (2024.naacl-long)
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| Challenge: | Recent studies have conjectured that multimodal hallucination is due to the vision encoder failing to ground on the image properly. |
| Approach: | They propose a multimodal self-feedback guided revision model that leverages visual cues to generate feedback to its initial response based on the visual information provided by the vision encoder. |
| Outcome: | The proposed model reduces multimodal hallucination and outperforms previous models on MMHal-Bench, POPE, and GAVIE. |
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation (2025.coling-main)
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| Challenge: | Large multimodal models struggle with text-rich images because of inadequate training data. |
| Approach: | They propose to use annotations from human annotators to generate instruction data by a hybrid approach to generate text prompts for large language models. |
| Outcome: | The proposed model improves multimodal alignment for text-rich images by using human annotations and tailored text prompts for large language models. |
PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns (2024.findings-acl)
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| Challenge: | recognizing patterns and abstracting concepts are key to general intelligence, we show . state-of-the-art large multimodal models struggle to generalize well to simple abstract patterns . |
| Approach: | They evaluate large multimodal models with abstract patterns based on colors, numbers, sizes, and shapes. |
| Outcome: | The proposed model fails to generalize well to simple abstract patterns, the study shows . the model fails on single-concept puzzles, despite its sophistication . |
A Culturally-diverse Multilingual Multimodal Video Benchmark & Model (2025.emnlp-main)
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Bhuiyan Sanjid Shafique, Ashmal Vayani, Muhammad Maaz, Hanoona Abdul Rasheed, Dinura Dissanayake, Mohammed Irfan Kurpath, Yahya Hmaiti, Go Inoue, Jean Lahoud, Md. Safirur Rashid, Shadid Intisar Quasem, Maheen Fatima, Franco Vidal, Mykola Maslych, Ketan Pravin More, Sanoojan Baliah, Hasindri Watawana, Yuhao Li, Fabian Farestam, Leon Schaller, Roman Tymtsiv, Simon Weber, Hisham Cholakkal, Ivan Laptev, Shin’ichi Satoh, Michael Felsberg, Mubarak Shah, Salman Khan, Fahad Shahbaz Khan
| Challenge: | Large multimodal models have gained attention for their effectiveness to understand and generate descriptions of visual content. |
| Approach: | They propose a multilingual Video LMM benchmark to evaluate video LMMs across 14 languages . they also introduce a machine translated multilingual video training set . |
| Outcome: | The proposed video LMM benchmark is designed to evaluate video Lmms across 14 languages including Arabic, Bengali, Chinese, English, French, German, Hindi, Japanese, Russian, Sinhala, Spanish, Swedish, Tamil, and Urdu. |
UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces (2025.acl-long)
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Baining Zhao, Jianjie Fang, Zichao Dai, Ziyou Wang, Jirong Zha, Weichen Zhang, Chen Gao, Yue Wang, Jinqiang Cui, Xinlei Chen, Yong Li
| Challenge: | Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored. |
| Approach: | They propose a benchmark to evaluate whether large multimodal models can process continuous first-person visual observations like humans. |
| Outcome: | The proposed model can process first-person visual observations like humans, enabling recall, perception, reasoning, and navigation. |