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Visual Question Answering

288 papers

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Papers

Question-Instructed Visual Descriptions for Zero-Shot Video Answering ACL 2024
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering ACL 2024
JDocQA: Japanese Document Question Answering Dataset for Generative Language Models COLING 2024
DIEM: Decomposition-Integration Enhancing Multimodal Insights CVPR 2024
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding CVPR 2024
Mask4Align: Aligned Entity Prompting with Color Masks for Multi-Entity Localization Problems CVPR 2024
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis EMNLP 2024
UOUO: Uncontextualized Uncommon Objects for Measuring Knowledge Horizons of Vision Language Models EMNLP 2024
TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering EMNLP 2024
Large Language Models Know What is Key Visual Entity: An LLM-assisted Multimodal Retrieval for VQA EMNLP 2024
CommVQA: Situating Visual Question Answering in Communicative Contexts EMNLP 2024
ECIS-VQG: Generation of Entity-centric Information-seeking Questions from Videos EMNLP 2024
FineCops-Ref: A new Dataset and Task for Fine-Grained Compositional Referring Expression Comprehension EMNLP 2024
ERVQA: A Dataset to Benchmark the Readiness of Large Vision Language Models in Hospital Environments EMNLP 2024
Multi-Level Information Retrieval Augmented Generation for Knowledge-based Visual Question Answering EMNLP 2024
The Illusion of Competence: Evaluating the Effect of Explanations on Users’ Mental Models of Visual Question Answering Systems EMNLP 2024
Visual Text Matters: Improving Text-KVQA with Visual Text Entity Knowledge-aware Large Multimodal Assistant EMNLP 2024
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models EMNLP 2024
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM EMNLP 2024
Exploring Question Guidance and Answer Calibration for Visually Grounded Video Question Answering EMNLP 2024
A Robust Dual-debiasing VQA Model based on Counterfactual Causal Effect EMNLP 2024
Plot Twist: Multimodal Models Don’t Comprehend Simple Chart Details EMNLP 2024
Not (yet) the whole story: Evaluating Visual Storytelling Requires More than Measuring Coherence, Grounding, and Repetition EMNLP 2024
ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering EMNLP 2024
Unraveling the Truth: Do VLMs really Understand Charts? A Deep Dive into Consistency and Robustness EMNLP 2024