AICA-Bench: Holistically Examining the Capabilities of VLMs in Affective Image Content Analysis (2026.findings-acl)
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| Challenge: | Recent studies have focused on factual correctness, semantic grounding, visual reasoning, or multimodal large language models. |
| Approach: | They propose a benchmark to assess AICA, which integrates perception, reasoning, and generation into a unified framework. |
| Outcome: | The proposed framework corrects intensity errors and significantly enhances descriptive depth. |
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| Challenge: | Existing text-rich image understanding benchmarks lack scale and fragmented scenarios . a new full-image structured output format is proposed to enable fine-grained evaluation of perception and reasoning capabilities. |
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| Challenge: | Existing LVLMs perform visual perception at multiple levels, but they are not able to perform multi-level tasks. |
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| Challenge: | PlotGen-Bench evaluates vision-language models' ability to generate executable visualization code from plots under realistic and complex visualization requirements. |
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Benchmarking Vision Language Models for Cultural Understanding (2024.emnlp-main)
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Shravan Nayak, Kanishk Jain, Rabiul Awal, Siva Reddy, Sjoerd Steenkiste, Lisa Hendricks, Karolina Stanczak, Aishwarya Agrawal
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| Challenge: | Recent advances in Multimodal Large Language Models (MLLMs) have shifted visual reasoning from tool-calling to end-to-end perceptionreasoning. |
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Almieyar-Oryx-BloomBench: A Bilingual Multimodal Benchmark for Cognitively Informed Evaluation of Vision-Language Models (2026.findings-acl)
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Mohammad Mahdi Abootorabi, Omid Ghahroodi, Anas Madkoor, Marzia Nouri, Doratossadat Dastgheib, Ehsaneddin Asgari
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Evaluating Vision-Language Models for Emotion Recognition (2025.findings-naacl)
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| Challenge: | Large Vision-Language Models (VLMs) have been used for objective multimodal reasoning tasks for decades. |
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