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arXiv Computer Vision

A-PAIR: A Benchmark and Identity-Consistent Grounding Framework for Air-Ground Cross-View Referring Person Detection

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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
llmsroboticsmultimodalbenchmarks
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arXiv Computer Vision
4d ago

Grounded Human-Attributed Description and Activity Recognition in Videos

The paper introduces Grounded Human-Attributed Description and Activity Recognition (GHADAR), a task that requires describing the characteristics and activities of every person in a video given their locations. It presents a new dataset derived from AVA-Actions with open‑set captions, a method that constrains cross‑attention masks in vision‑language models to better use grounding information, and a holistic VLM‑based evaluation schema that compares concepts directly between video and predictions. Experiments show the proposed method surpasses state‑of‑the‑art VLMs and highlight limitations of existing evaluation metrics.

By Jay N. Paranjape, Jurijs Nazarovs, Rohan Sarkar, Yue Guo, Lipeng Ke, Sankar Venkataraman, Vishal M. Patel, Nataraj Jammalamadaka
multimodalbenchmarks
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arXiv Computer Vision
Sep 22

DeCo: Efficient Decouple-to-Couple Learning for Multi-Task Visual Grounding

DeCo introduces an efficient decouple-to-couple learning framework for multi-task visual grounding, addressing conflicts between localization and segmentation tasks. It first applies Task-aware Semantic Decoupling (TSD) to separate shared visual cues into task-specific features guided by salient words, then uses Hybrid Prior Coupling (HPC) to merge sentence-level semantic priors with mask-derived spatial priors for improved grounding. Experiments across multiple natural and remote sensing datasets show that DeCo achieves state‑of‑the‑art performance while requiring only lightweight trainable parameters on a frozen multimodal encoder.

By Xiaoqiang Lu, Licheng Jiao, Long Sun, Yuting Yang, Xu Liu, Lingling Li, Wenping Ma, Fang Liu
llmscomputer-visionmultimodalbenchmarks
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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
llmsmultimodalbenchmarks
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