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| Adversarial | vqa | : A New Benchmark for Evaluating the Robustness of VQA Models |
| Adversarial | vqa | : A New Benchmark for Evaluating the Robustness of VQA Models |
| Beyond OCR + | vqa | : Towards end-to-end reading and reasoning for robust and accurate textvqa |
| Beyond | vqa | : Generating Multi-word Answers and Rationales to Visual Questions |
| Blind | vqa | on 360° Video via Progressively Learning From Pixels, Frames, and Video |
| Can you even tell left from right? Presenting a new challenge for | vqa | |
| cascaded long short-term memory (LSTM) driven generic visual question answering ( | vqa | ), A |
| Context- | vqa | : Towards Context-Aware and Purposeful Visual Question Answering |
| Contrast and Classify: Training Robust | vqa | Models |
| Coordinating explicit and implicit knowledge for knowledge-based | vqa | |
| Counterfactual | vqa | : A Cause-Effect Look at Language Bias |
| CRIC: A | vqa | Dataset for Compositional Reasoning on Vision and Commonsense |
| CS- | vqa | : Visual Question Answering with Compressively Sensed Images |
| Doc | vqa | : A Dataset for VQA on Document Images |
| Domain-robust | vqa | with diverse datasets and methods but no target labels |
| Encyclopedic | vqa | : Visual questions about detailed properties of fine-grained categories |
| Explaining | vqa | predictions using visual grounding and a knowledge base |
| Explanation vs. attention: A two-player game to obtain attention for | vqa | and visual dialog |
| Exploring Sparse Spatial Relation in Graph Inference for Text-Based | vqa | |
| FAST- | vqa | : Efficient End-to-End Video Quality Assessment with Fragment Sampling |
| FGC | vqa | : Fine-Grained Cross-Attention for Medical VQA |
| From Strings to Things: Knowledge-Enabled | vqa | Model That Can Read and Reason |
| HIDRO- | vqa | : High Dynamic Range Oracle for Video Quality Assessment |
| How to Make a BLT Sandwich? Learning | vqa | towards Understanding Web Instructional Videos |
| How to Practice | vqa | on a Resource-limited Target Domain |
| How Transferable are Reasoning Patterns in | vqa | ? |
| Improving relevant subjective testing for validation: Comparing machine learning algorithms for finding similarities in | vqa | datasets using objective measures |
| Inductive Biases for Low Data | vqa | : A Data Augmentation Approach |
| Inverse Visual Question Answering: A New Benchmark and | vqa | Diagnosis Tool |
| IQ- | vqa | : Intelligent Visual Question Answering |
| Knowledge Informed Sequential Scene Graph Verification Using | vqa | |
| KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based | vqa | |
| LaTr: Layout-Aware Transformer for Scene-Text | vqa | |
| Localize, Group, and Select: Boosting Text- | vqa | by Scene Text Modeling |
| Loss Re-Scaling | vqa | : Revisiting the Language Prior Problem From a Class-Imbalance View |
| Making the V in Text- | vqa | Matter |
| Making the V in | vqa | Matter: Elevating the Role of Image Understanding in Visual Question Answering |
| MD- | vqa | : Multi-Dimensional Quality Assessment for UGC Live Videos |
| MRA-Net: Improving | vqa | Via Multi-Modal Relation Attention Network |
| MUST- | vqa | : Multilingual Scene-Text VQA |
| MUST- | vqa | : Multilingual Scene-Text VQA |
| NExT-OOD: Overcoming Dual Multiple-Choice | vqa | Biases |
| Object-Based Reasoning in | vqa | |
| OK- | vqa | : A Visual Question Answering Benchmark Requiring External Knowledge |
| Optimizing feature pooling and prediction models of | vqa | algorithms |
| Perception Matters: Detecting Perception Failures of | vqa | Models Using Metamorphic Testing |
| POP- | vqa | : Privacy preserving, On-device, Personalized Visual Question Answering |
| PromptCap: Prompt-Guided Image Captioning for | vqa | with GPT-3 |
| Prompting large language model with context and pre-answer for knowledge-based | vqa | |
| Q: How to Specialize Large Vision-Language Models to Data-Scarce | vqa | Tasks? A: Self-Train on Unlabeled Images! |
| RankD | vqa | : Deep VQA based on Ranking-inspired Hybrid Training |
| Reducing Vision-Answer Biases for Multiple-Choice | vqa | |
| Roses are Red, Violets are Blue… But Should | vqa | expect Them To? |
| S3C: Semi-Supervised | vqa | Natural Language Explanation via Self-Critical Learning |
| SB- | vqa | : A Stack-Based Video Quality Assessment Framework for Video Enhancement |
| Self-supervised knowledge distillation in counterfactual learning for | vqa | |
| Sim | vqa | : Exploring Simulated Environments for Visual Question Answering |
| SQuINTing at | vqa | Models: Introspecting VQA Models With Sub-Questions |
| SQuINTing at | vqa | Models: Introspecting VQA Models With Sub-Questions |
| Suppressing Biased Samples for Robust | vqa | |
| TA-Student | vqa | : Multi-Agents Training by Self-Questioning |
| TAP: Text-Aware Pre-training for Text- | vqa | and Text-Caption |
| Towards Causal | vqa | : Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing |
| Towards | vqa | Models That Can Read |
| UGC- | vqa | : Benchmarking Blind Video Quality Assessment for User Generated Content |
| Understanding | vqa | for Negative Answers Through Visual and Linguistic Inference |
| VC- | vqa | : Visual Calibration Mechanism For Visual Question Answering |
| VDPVE: | vqa | Dataset for Perceptual Video Enhancement |
| Video Question Answering, Movies, Spatio-Temporal, Query, | vqa | |
| Vision-Language Models, Language-Vision Models, | vqa | |
| Visual Question Answering, Query, | vqa | |
| vqa | as a factoid question answering problem: A novel approach for knowledge-aware and explainable visual question answering |
| vqa | Therapy: Exploring Answer Differences by Visually Grounding Answers |
| vqa | With No Questions-Answers Training |
| vqa | , Visual Question Answering, Neural Networks |
| vqa | -E: Explaining, Elaborating, and Enhancing Your Answers for Visual Questions |
| vqa | -GNN: Reasoning with Multimodal Knowledge via Graph Neural Networks for Visual Question Answering |
| vqa | -LOL: Visual Question Answering Under the Lens of Logic |
| vqa | -Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions, The |
| vqa | : Visual Question Answering |
| vqa | : Visual Question Answering |
| vqa | : Visual Question Answering |
| VQS: Linking Segmentations to Questions and Answers for Supervised Attention in | vqa | and Question-Focused Semantic Segmentation |
| Weakly Supervised Grounding for | vqa | in Vision-Language Transformers |
| Zoom- | vqa | : Patches, Frames and Clips Integration for Video Quality Assessment |
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