Beyond Embeddings: The Promise of Visual Table in Visual Reasoning (2024.emnlp-main)
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| Challenge: | Visual representation learning has been a cornerstone in computer vision for decades. |
| Approach: | They propose a visual representation tailored for visual reasoning that provides instance-level world knowledge and detailed attributes that are essential for visual reason. |
| Outcome: | The proposed visual tables outperform existing models on 11 visual reasoning benchmarks. |
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| Challenge: | Recent advances in language and vision assistants have showcased impressive capabilities but suffer from a lack of transparency, limiting broader research and reproducibility. |
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TableVista: Benchmarking Multimodal Table Reasoning under Visual and Structural Complexity (2026.findings-acl)
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Zheyuan Yang, Liqiang Shang, Junjie Chen, Xun Yang, Chenglong Xu, Bo Yuan, Chenyuan Jiao, Yaoru Sun, Yilun Zhao
| Challenge: | TableVista evaluates multimodal table reasoning under visual and structural complexity . current models struggle to maintain reasoning consistency when structural complexity combined with visually integrated presentations. |
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Multimodal Logical Inference System for Visual-Textual Entailment (P19-2)
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| Challenge: | Recent studies of multimodal inference provide challenging tasks such as visual question answering and visual reasoning. |
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated their remarkable capabilities in natural language understanding and generation, but they struggle with formal logical reasoning. |
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| Challenge: | Logic2Vision is a visual question-answering dataset that validates question authenticity with the corresponding image and then reasoning over it. |
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| Challenge: | Existing models that use natural language rationales provide intuitive, higher-level explanations that are easily understandable by humans. |
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Visual–Linguistic Abductive Reasoning with LLMs for Knowledge-based Visual Question Answering (2026.findings-eacl)
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| Challenge: | Recent efforts to leverage large language models for reasoning focus on visual perception and language reasoning as separate processes. |
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| Challenge: | Existing OCR-free models struggle with complex table layouts and formatting. |
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Knowledge Image Matters: Improving Knowledge-Based Visual Reasoning with Multi-Image Large Language Models (2025.acl-long)
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| Challenge: | Knowledge-based visual reasoning (KB-VR) is a challenging task, as it requires machines not only to understand concepts and relationships of visual scenes, but also to associate them with external world knowledge to perform chain of reasoning on open-world questions. |
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