Cover Image

NegVQA: Can Vision Language Models Understand Negation?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Yuhui, Su, Yuchang, Liu, Yiming, Yeung-Levy, Serena
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Computers and Society Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
by: Zhang, Yuhui, et al.
Published: (2025)

Why are Visually-Grounded Language Models Bad at Image Classification?
by: Zhang, Yuhui, et al.
Published: (2024)

VideoAgent: Long-form Video Understanding with Large Language Model as Agent
by: Wang, Xiaohan, et al.
Published: (2024)

Data or Language Supervision: What Makes CLIP Better than DINO?
by: Liu, Yiming, et al.
Published: (2025)

RadDiff: Describing Differences in Radiology Image Sets with Natural Language
by: Shen, Xiaoxian, et al.
Published: (2026)

Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data
by: Zhang, Yuhui, et al.
Published: (2024)

Can MLLMs Understand the Deep Implication Behind Chinese Images?
by: Zhang, Chenhao, et al.
Published: (2024)

μ-Bench: A Vision-Language Benchmark for Microscopy Understanding
by: Lozano, Alejandro, et al.
Published: (2024)

Temporal Preference Optimization for Long-Form Video Understanding
by: Li, Rui, et al.
Published: (2025)

Transductive Visual Programming: Evolving Tool Libraries from Experience for Spatial Reasoning
by: Wu, Shengguang, et al.
Published: (2025)

Toward Effective Reinforcement Learning Fine-Tuning for Medical VQA in Vision-Language Models
by: Zhu, Wenhui, et al.
Published: (2025)

Can Vision Language Models Understand Mimed Actions?
by: Cho, Hyundong, et al.
Published: (2025)

VTCBench: Can Vision-Language Models Understand Long Context with Vision-Text Compression?
by: Zhao, Hongbo, et al.
Published: (2025)

Identifying Implicit Social Biases in Vision-Language Models
by: Hamidieh, Kimia, et al.
Published: (2024)

Vision-Language Models under Cultural and Inclusive Considerations
by: Karamolegkou, Antonia, et al.
Published: (2024)

Beyond Translation: Cross-Cultural Meme Transcreation with Vision-Language Models
by: Zhao, Yuming, et al.
Published: (2026)

Cultural Awareness in Vision-Language Models: A Cross-Country Exploration
by: Madasu, Avinash, et al.
Published: (2025)

KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language
by: Kim, Yoonshik, et al.
Published: (2025)

Closing the Gap: Data-Centric Fine-Tuning of Vision Language Models for the Standardized Exam Questions
by: Sert, Egemen, et al.
Published: (2025)

AdaNeg: Adaptive Negative Proxy Guided OOD Detection with Vision-Language Models
by: Zhang, Yabin, et al.
Published: (2024)

Describing Differences in Image Sets with Natural Language
by: Dunlap, Lisa, et al.
Published: (2023)

Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models
by: Sui, Elaine, et al.
Published: (2024)

MangaVQA and MangaLMM: A Benchmark and Specialized Model for Multimodal Manga Understanding
by: Baek, Jeonghun, et al.
Published: (2025)

ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
by: Wang, Xingqi, et al.
Published: (2025)

MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research
by: Burgess, James, et al.
Published: (2025)

Multimodal Retrieval-Augmented Generation with Large Language Models for Medical VQA
by: Karim, A H M Rezaul, et al.
Published: (2025)

Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA
by: Fan, Yue, et al.
Published: (2024)

Omni-NegCLIP: Enhancing CLIP with Front-Layer Contrastive Fine-Tuning for Comprehensive Negation Understanding
by: Xu, Jingqi
Published: (2026)

Benchmarking Vision Language Models for Cultural Understanding
by: Nayak, Shravan, et al.
Published: (2024)

Closing the Modality Gap for Mixed Modality Search
by: Li, Binxu, et al.
Published: (2025)

Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding
by: Elhenawy, Mohammed, et al.
Published: (2025)

Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations
by: Srivastava, Archita, et al.
Published: (2025)

Vision Language Models in Medicine
by: Kalpelbe, Beria Chingnabe, et al.
Published: (2025)

Can Vision-Language Models Solve Visual Math Equations?
by: Choudhury, Monjoy Narayan, et al.
Published: (2025)

FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding
by: Faure, Gueter Josmy, et al.
Published: (2026)

Do VLMs Have a Moral Backbone? A Study on the Fragile Morality of Vision-Language Models
by: Liu, Zhining, et al.
Published: (2026)

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models
by: Xia, Peng, et al.
Published: (2024)

REBUS: A Robust Evaluation Benchmark of Understanding Symbols
by: Gritsevskiy, Andrew, et al.
Published: (2024)

VideoNorms: Benchmarking Cultural Awareness of Video Language Models
by: Varimalla, Nikhil Reddy, et al.
Published: (2025)

Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey
by: Parraga, Otávio, et al.
Published: (2022)