AUVIC: Adversarial Unlearning of Visual Concepts for Multi-modal Large Language Models

DomainCQA is a proposed framework aimed at enhancing Chart Question Answering (CQA) by focusing on both visual comprehension and knowledge-intensive reasoning. Current benchmarks primarily assess superficial parsing of chart data, neglecting deeper scientific reasoning. The framework has been applied to astronomy, resulting in AstroChart, which includes 1,690 QA pairs across 482 charts. This benchmark reveals significant weaknesses in fine-grained perception, numerical reasoning, and domain knowledge integration among 21 Multimodal Large Language Models (MLLMs).

AirCopBench is a new benchmark introduced to evaluate Multimodal Large Language Models (MLLMs) in multi-drone collaborative perception tasks. It addresses the lack of comprehensive evaluation tools for multi-agent systems, which outperform single-agent setups in terms of coverage and robustness. The benchmark includes over 14,600 questions across various task dimensions, such as Scene Understanding and Object Understanding, designed to assess performance under challenging conditions.

VP-Bench is a newly introduced benchmark designed to evaluate the ability of multimodal large language models (MLLMs) to interpret visual prompts (VPs) in images. This benchmark addresses a significant gap in existing evaluations, as no systematic assessment of MLLMs' effectiveness in recognizing VPs has been conducted. VP-Bench utilizes a two-stage evaluation framework, involving 30,000 visualized prompts across eight shapes and 355 attribute combinations, to assess MLLMs' capabilities in VP perception and utilization.

The article discusses CyPortQA, a new multimodal benchmark designed to enhance cyclone preparedness in U.S. port operations. As tropical cyclones become more intense and forecasts less certain, U.S. ports face increased supply-chain risks. CyPortQA integrates diverse forecast products, including wind maps and advisories, to provide actionable guidance. It compiles 2,917 real-world disruption scenarios from 2015 to 2023, covering 145 principal U.S. ports and 90 named storms, aiming to improve the accuracy and reliability of multimodal large language models (MLLMs) in this context.

The article presents a new framework called Hindsight Distilled Reasoning (HinD) with Knowledge Encouragement Preference Optimization (KEPO) aimed at enhancing Knowledge-based Visual Question Answering (KBVQA). This framework addresses the limitations of existing methods that rely on implicit reasoning in multimodal large language models (MLLMs). By prompting a 7B-size MLLM to complete reasoning processes, the framework aims to improve the integration of external knowledge in visual question answering tasks.

MOSABench is a newly introduced evaluation dataset aimed at addressing the lack of standardized benchmarks for multi-object sentiment analysis in multimodal large language models (MLLMs). It comprises approximately 1,000 images featuring multiple objects, requiring MLLMs to evaluate the sentiment of each object independently. Key features of MOSABench include distance-based target annotation and an improved scoring mechanism, highlighting current limitations in MLLMs' performance in this complex task.