Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?
As Multimodal Large Language Models (MLLMs) continue to evolve, their cognitive and reasoning capabilities have seen remarkable progress. However, challenges in visual fine-grained perception and commonsense causal inference persist. This paper introduces Argus Inspection, a multimodal benchmark with two levels of difficulty, emphasizing detailed visual recognition while incorporating real-world commonsense understanding to evaluate causal reasoning abilities. Expanding on it, we present the Eye of Panoptes framework, which integrates a binary parametric Sigmoid metric with an indicator function, enabling a more holistic evaluation of MLLMs' responses in opinion-based reasoning tasks. Experiments conducted on 26 mainstream MLLMs reveal that the highest performance in visual fine-grained reasoning reaches only 0.46, highlighting considerable potential for enhancement. Our research offers valuable perspectives for the continued refinement of MLLMs.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Argus: Vision-Centric Reasoning with Grounded Chain-of-Thought
Recent advances in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in vision-language tasks, yet they often struggle with vision-centric scenarios where precise visual focus is needed f…
Multimodal ReasoningArgus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation
Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between the visual features needed for these two …
Argus: Leveraging Multiview Images for Improved 3-D Scene Understanding With Large Language Models
Advancements in foundation models have made it possible to conduct applications in various downstream tasks. Especially, the new era has witnessed a remarkable capability to extend Large Language Models (LLMs) for tackli…
3D Point Cloud ReconstructionPoint cloud reconstructionScene UnderstandingARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Texts
Can narratives make arguments more persuasive? And to this end, which narrative features matter most? Although stories are often seen as powerful tools for persuasion, their specific role in online, unstructured argument…
Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations
Difference-in-differences (DID) studies are widely used to evaluate climate policy, but assessing the evidence supporting their identification assumptions remains challenging. We introduce ARGUS, a structured language-mo…