Measuring Progress in Fine-grained Vision-and-Language Understanding (2023.acl-long)
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| Challenge: | X-VLM models lack "fine-grained" understanding of relationships, verbs and numbers in images . pretraining on large-scale image–text data from the Web has facilitated rapid progress on many vision-and-language tasks . |
| Approach: | They investigate models that outperform other baselines on fine-grained data . they highlight importance of novel losses and rich data sources for learning fine-grain skills . |
| Outcome: | The proposed model outperforms baseline models on four fine-grained benchmarks . the model outpersforms other baseline models and even degrades performance . |
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| Challenge: | Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease. |
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| Challenge: | Vision-language models have broad competence that is difficult to evaluate . current evaluation benchmarks focus on only assessing one or a few capabilities . |
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MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)
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| Challenge: | Vision-Language Models (VLMs) have shown promise as web agents, yet their planning has been overlooked. |
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| Challenge: | Recent vision-language models are being used for downstream tasks that require large datasets and supervised datasets. |
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Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model (2025.acl-long)
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Gregor Geigle, Florian Schneider, Carolin Holtermann, Chris Biemann, Radu Timofte, Anne Lauscher, Goran Glavaš
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| Challenge: | Recent work has re-surfaced a concern that has long plagued vision-language models: poor performance on simple tasks like attribute attachment, counting, etc. |
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| Challenge: | Large pretrained language models (LMs) have been criticized for lack of grounding, i.e., connecting words to their meanings in the physical world. |
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VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models (2026.findings-acl)
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| Challenge: | ***VLURes** provides a practical testbed for long-text grounding and multilingual robustness in web-realistic agent settings. |
| Approach: | They propose a multilingual benchmark for evaluating vision-language models under long-text grounding. |
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