arXiv AI
Aug 26

When Seeing Is Not Enough: Benchmarking Interactive Visual Grounding in LVLMs

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.

By Zhengxiang Wang, Owen Rambow
arXiv Computation and Language
Sep 17

PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection

PANORAMA introduces a new panoptic grounded captioning framework that jointly generates detailed image captions and associates each phrase with precise pixel-level masks. The authors create PanoCaps, a human‑annotated benchmark with dense captions and near‑complete pixel coverage, and propose a phrase‑mask matching protocol with a generalized Panoptic Quality metric. PANORAMA conditions a pretrained segmenter on contextualized phrase representations, learns to select appropriate masks, and achieves state‑of‑the‑art grounding performance on PanoCaps and other pixel‑level tasks.

By Sara Pieri, Evangelos Kazakos, Shizhe Chen, Josef Sivic, Cordelia Schmid
arXiv Computer Vision
Sep 3

Towards Open-World Referring Expression Comprehension: A Benchmark with Training-free Multi-task Consistency Checker

The paper introduces OpenRef, a benchmark for Referring Expression Comprehension (REC) designed for open‑world scenarios. OpenRef expands beyond simple settings by including diverse visual domains, variable target counts (multi‑target and none‑target), and a rich vocabulary with proper nouns, polysemous words, and ordinal terms. It also proposes new evaluation metrics—F1 for grounding accuracy and N3R for negative expression rejection—and presents a training‑free Multi‑task Consistency Checker (MCC) that improves model performance with a single click.

By Zongjian Wu, Lei Zhang