Evaluating Visual Language Models on Visual Grounding for Visually Impaired People
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2502.04192v5 Announce Type: replace Abstract: Multiple works have emerged to push the boundaries of multi-modal large language models (MLLMs) towards pixel-level understanding. The current tren...
The paper introduces a controlled evaluation framework for interactive visual grounding in large vision-language models (LVLMs), examining how varying amounts of initial target information and dialogue affect performance. Experiments across four visual contexts and interaction protocols show that current LVLMs lag behind human baselines, especially when no initial description is given and information must be gathered through questions. The study also finds that LVLMs are poorly calibrated, often overestimating confidence, and that interactive grounding remains a significant challenge requiring visual matching, information seeking, and synthesis.
MagiC is a benchmark for grounded multimodal cognition that evaluates vision‑language models on answer accuracy, step‑by‑step reasoning quality, grounding fidelity, and self‑correction. It contains about 5,500 weakly supervised QA examples generated from strong model outputs and 900 human‑curated examples with fine‑grained annotations, including rationales and bounding box groundings. The benchmark tests 15 models ranging from 7B to 70B parameters, introduces new metrics such as MagiScore and StepSense, and includes diagnostic settings to probe robustness against adversarial visual cues and introspective error correction.
High benchmark accuracy does not guarantee genuine use of visual evidence. We study this problem in traffic accident Video Question Answering (VideoQA), where correct answers should depend on scene-specific visual evidence but may instead be inferred from textual shortcuts.
arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...
arXiv:2607. 08745v1 Announce Type: new Abstract: Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as scene understanding, decision making, trajectory prediction, and visual question answering.