arXiv Computer Vision September 3, 2026 By Zongjian Wu, Lei Zhang

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

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arXiv AI

Jul 29

RefBench-PRO: Perceptual and Reasoning Oriented Benchmark for Referring Expression Comprehension

arXiv:2512. 06276v3 Announce Type: replace-cross Abstract: Referring Expression Comprehension (REC) is a vision-language task that localizes a specific image region based on a textual description.

By Tianyi Gao, Hao Li, Han Fang, Xin Wei, Xiaodong Dong, Hongbo Sun, Ye Yuan, Zhongjiang He, Jinglin Xu, Jingmin Xin, Hao Sun

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jul 8

OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding

arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.

By Wenyuan Huang, Zhenyu Zhang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang

llms benchmarks

arXiv Computer Vision

3d ago

What is the Right Embedding Space for Contrastive Learning in Referring Expression Counting?

arXiv:2505.22850v3 Announce Type: replace Abstract: Referring Expression Counting (REC) requires distinguishing visually similar objects described by fine-grained text cues. Existing methods tackle t...

By Kostas Triaridis, Panagiotis Kaliosis, E-Ro Nguyen, Jingyi Xu, Dimitris Samaras, Hieu Le

rag multimodal benchmarks safety

arXiv Computer Vision

Aug 25

OvDSGG: End-to-End Open-Vocabulary Dynamic Scene Graph Generation

arXiv:2608.14835v2 Announce Type: replace Abstract: Dynamic scene graphs (DSGs) capture spatio-temporal interactions across videos as $\langle$subject, predicate, object$\rangle$ triplets, and underp...

By John Helsby, Yi Yang, Bodo Rosenhahn, Michael Ying Yang

llms nlp efficiency benchmarks safety

arXiv Machine Learning

3d ago

LIBERO-Para: A Diagnostic Benchmark and Metrics for Paraphrase Robustness in VLA Models

arXiv:2603.28301v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models achieve strong performance in robotic manipulation by leveraging pre-trained vision-language backbones. However...

By Chanyoung Kim, Minwoo Kim, Minseok Kang, Hyunwoo Kim, Dahuin Jung

robotics fine-tuning multimodal benchmarks

Hugging Face Trending Papers

Jul 23

CRAG-MM-Diagnostics: Enabling Stage-Wise Analysis of Knowledge-Intensive VQA

Knowledge-Intensive Visual Question Answering (KI-VQA) benchmarks evaluate Vision-Language Models (VLMs) as multimodal knowledge assistants by requiring external information beyond a provided image to answer questions. KI-VQA involves multiple sub-problems -referring expression understanding, visual grounding, object recognition, knowledge retrieval, and reasoning-yet existing benchmarks typically report only end-task accuracy, obscuring where failures arise.

llms rag nlp multimodal benchmarks