Cover Image

Seeing Isn't Believing: Uncovering Blind Spots in Evaluator Vision-Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Khan, Mohammed Safi Ur Rahman, Suryanarayanan, Sanjay, Anand, Tushar, Khapra, Mitesh M.
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Can Vision-Language Models Evaluate Handwritten Math?
by: Nath, Oikantik, et al.
Published: (2025)

Finding Blind Spots in Evaluator LLMs with Interpretable Checklists
by: Doddapaneni, Sumanth, et al.
Published: (2024)

Seeing Isn't Believing: Context-Aware Adversarial Patch Synthesis via Conditional GAN
by: Kazoom, Roie, et al.
Published: (2025)

Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?
by: Zhang, Yue, et al.
Published: (2026)

Seeing Isn't Always Believing: Analysis of Grad-CAM Faithfulness and Localization Reliability in Lung Cancer CT Classification
by: Panboonyuen, Teerapong
Published: (2026)

Pralekha: Cross-Lingual Document Alignment for Indic Languages
by: Suryanarayanan, Sanjay, et al.
Published: (2024)

Cross-Lingual Auto Evaluation for Assessing Multilingual LLMs
by: Doddapaneni, Sumanth, et al.
Published: (2024)

FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypes
by: Nawale, Janki Atul, et al.
Published: (2025)

Seeing Isn't Believing: Addressing the Societal Impact of Deepfakes in Low-Tech Environments
by: Wasi, Azmine Toushik, et al.
Published: (2025)

Seeing Isn't Believing: Mitigating Belief Inertia via Active Intervention in Embodied Agents
by: Wang, Hanlin, et al.
Published: (2026)

Seeing Syntax: Uncovering Syntactic Learning Limitations in Vision-Language Models
by: Dumpala, Sri Harsha, et al.
Published: (2024)

Seeing No Evil: Blinding Large Vision-Language Models to Safety Instructions via Adversarial Attention Hijacking
by: Li, Jingru, et al.
Published: (2026)

Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models
by: Wu, Jiaying, et al.
Published: (2025)

Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
by: Deng, Ailin, et al.
Published: (2024)

Seeing Isn't Orienting: A Cognitively Grounded Benchmark Reveals Systematic Orientation Failures in MLLMs
by: Tasnim, Nazia, et al.
Published: (2025)

Seeing is Believing: Rich-Context Hallucination Detection for MLLMs via Backward Visual Grounding
by: Guo, Pinxue, et al.
Published: (2025)

Seeing Isn't Orienting: A Cognitively Grounded Benchmark Reveals Systematic Orientation Failures in MLLMs Supplementary
by: Tasnim, Nazia, et al.
Published: (2026)

Towards Building Large Scale Datasets and State-of-the-Art Automatic Speech Translation Systems for 14 Indian Languages
by: Sankar, Ashwin, et al.
Published: (2024)

Preferences of a Voice-First Nation: Large-Scale Pairwise Evaluation and Preference Analysis for TTS in Indian Languages
by: Anand, Srija, et al.
Published: (2026)

Seeing is Believing? Enhancing Vision-Language Navigation using Visual Perturbations
by: Zhang, Xuesong, et al.
Published: (2024)

Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?
by: Laskar, Md Tahmid Rahman, et al.
Published: (2025)

"See the World, Discover Knowledge": A Chinese Factuality Evaluation for Large Vision Language Models
by: Gu, Jihao, et al.
Published: (2025)

Seeing is Believing: Emotion-Aware Audio-Visual Language Modeling for Expressive Speech Generation
by: Tan, Weiting, et al.
Published: (2025)

ColorBlindnessEval: Can Vision-Language Models Pass Color Blindness Tests?
by: Ling, Zijian, et al.
Published: (2025)

Are VLMs Seeing or Just Saying? Uncovering the Illusion of Visual Re-examination
by: Shi, Chufan, et al.
Published: (2026)

Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection
by: Wang, Tao, et al.
Published: (2023)

Seeing Through Their Eyes: Evaluating Visual Perspective Taking in Vision Language Models
by: Góral, Gracjan, et al.
Published: (2024)

Knowing the Answer Isn't Enough: Fixing Reasoning Path Failures in LVLMs
by: Wang, Chaoyang, et al.
Published: (2025)

ColorFoil: Investigating Color Blindness in Large Vision and Language Models
by: Samin, Ahnaf Mozib, et al.
Published: (2024)

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models
by: Xuan, Weihao, et al.
Published: (2025)

IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing
by: Nath, Oikantik, et al.
Published: (2025)

IndicIFEval: A Benchmark for Verifiable Instruction-Following Evaluation in 14 Indic Languages
by: Jayakumar, Thanmay, et al.
Published: (2026)

From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models
by: Wu, Juncheng, et al.
Published: (2026)

Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yet
by: Tariang, Diangarti, et al.
Published: (2024)

Arbitration Failure, Not Perceptual Blindness: How Vision-Language Models Resolve Visual-Linguistic Conflicts
by: Nooralahzadeh, Farhad, et al.
Published: (2026)

Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
by: He, Zhentao, et al.
Published: (2025)

The Illusion-Illusion: Vision Language Models See Illusions Where There are None
by: Ullman, Tomer
Published: (2024)

NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data
by: Javed, Tahir, et al.
Published: (2025)

IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages
by: Khan, Mohammed Safi Ur Rahman, et al.
Published: (2024)

Evaluating Vision-Language Models as Evaluators in Path Planning
by: Aghzal, Mohamed, et al.
Published: (2024)