When Does an Image Determine the Answer? Benchmarking Visual Answerability across Charts and Scenes

arXiv:2609.34480 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Abstention — 36× in this paper · explained in 1 episode
• Benchmark — 27× in this paper · explained in 2 episodes
• Hallucination — 8× in this paper · explained in 4 episodes
• Verification — 8× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Admissibility — 5× in this paper · explained in 2 episodes
• Benchmarking — 5× in this paper · explained in 4 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Vision-language models — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Conformal Prediction — 2× in this paper · explained in 5 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Compositional Generalization — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Error Decomposition — 1× in this paper · explained in 3 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Hallucination Detection — 1× in this paper · explained in 2 episodes
• SMT solver — 1× in this paper · explained in 2 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
• VQA: Visual Question Answering
• Analyzing the Behavior of Visual Question Answering Models
• Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
• Neural Module Networks
• A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
• Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
• CLOSURE: Assessing Systematic Generalization of CLEVR Models
• Scene Text Visual Question Answering
• Querying Incomplete Numerical Data: Between Certain and Possible Answers
• Underspecification Presents Challenges for Credibility in Modern Machine Learning
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
• BLINK: Multimodal Large Language Models Can See but Not Perceive
• Datasheets for Datasets
• Selective Classification for Deep Neural Networks
• SelectiveNet: A Deep Neural Network with an Integrated Reject Option
• Shortcut Learning in Deep Neural Networks
• Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
• HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
• Detecting and Preventing Hallucinations in Large Vision Language Models

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models