SOTAVerified

Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

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Showing 1751–1800 of 2167 papers

TitleStatusHype
MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning—0
Modeling Coreference Relations in Visual Dialog—0
Modern Question Answering Datasets and Benchmarks: A Survey—0
Modular Graph Attention Network for Complex Visual Relational Reasoning—0
Modulated Self-attention Convolutional Network for VQA—0
MoEMoE: Question Guided Dense and Scalable Sparse Mixture-of-Expert for Multi-source Multi-modal Answering—0
Motion-Appearance Co-Memory Networks for Video Question Answering—0
MPDrive: Improving Spatial Understanding with Marker-Based Prompt Learning for Autonomous Driving—0
mR^2AG: Multimodal Retrieval-Reflection-Augmented Generation for Knowledge-Based VQA—0
MRET: Multi-resolution Transformer for Video Quality Assessment—0
Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering—0
Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA—0
Multi-Agents Based on Large Language Models for Knowledge-based Visual Question Answering—0
Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes—0
Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answering—0
MuLTI: Efficient Video-and-Language Understanding with Text-Guided MultiWay-Sampler and Multiple Choice Modeling—0
Multi-grained Attention with Object-level Grounding for Visual Question Answering—0
Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering—0
Multi-Level Attention Networks for Visual Question Answering—0
Multilevel Semantic-Aware Model for AI-Generated Video Quality Assessment—0
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks—0
Multimodal Commonsense Knowledge Distillation for Visual Question Answering—0
Multimodal Compact Bilinear Pooling for Multimodal Neural Machine Translation—0
Multimodal Continuous Visual Attention Mechanisms—0
Multi-modal Deep Analysis for Multimedia—0
Multimodal Differential Network for Visual Question Generation—0
Multimodal Few-Shot Learning with Frozen Language Models—0
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing—0
Multimodal Graph Networks for Compositional Generalization in Visual Question Answering—0
Multimodal grid features and cell pointers for Scene Text Visual Question Answering—0
Multi-Modal Hallucination Control by Visual Information Grounding—0
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis—0
Multimodal Integration of Human-Like Attention in Visual Question Answering—0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications—0
Multi-modality Latent Interaction Network for Visual Question Answering—0
Multimodal Learning and Reasoning for Visual Question Answering—0
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering—0
Multimodal Machine Learning: Integrating Language, Vision and Speech—0
Multimodal Neural Graph Memory Networks for Visual Question Answering—0
Multimodal Neural Machine Translation for Low-resource Language Pairs using Synthetic Data—0
Multimodal Reranking for Knowledge-Intensive Visual Question Answering—0
Multi-Modal Retrieval Augmentation for Open-Ended and Knowledge-Intensive Video Question Answering—0
Multimodal Unified Attention Networks for Vision-and-Language Interactions—0
Multiple-Question Multiple-Answer Text-VQA—0
Multi-Prompts Learning with Cross-Modal Alignment for Attribute-based Person Re-Identification—0
Multi-task Learning of Hierarchical Vision-Language Representation—0
MUST-VQA: MUltilingual Scene-text VQA—0
MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering—0
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples—0
Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey—0
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All datasetsGQA Test2019VQA v2 test-devVQA v2 test-stdOK-VQAMSVD-QADocVQA testMSRVTT-QAInfographicVQAGQA test-devVizWiz 2020 VQAA-OKVQACLEVR

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1humanAccuracy89.3—Unverified
2DREAM+Unicoder-VL (MSRA)Accuracy76.04—Unverified
3TRRNet (Ensemble)Accuracy74.03—Unverified
4MIL-nbgaoAccuracy73.81—Unverified
5Kakao BrainAccuracy73.33—Unverified
6Coarse-to-Fine Reasoning, Single ModelAccuracy72.14—Unverified
7270Accuracy70.23—Unverified
8NSM ensemble (updated)Accuracy67.55—Unverified
9VinVL-DPTAccuracy64.92—Unverified
10VinVL+LAccuracy64.85—Unverified
#ModelMetricClaimedVerifiedStatus
1PaLIAccuracy84.3—Unverified
2BEiT-3Accuracy84.19—Unverified
3VLMoAccuracy82.78—Unverified
4ONE-PEACEAccuracy82.6—Unverified
5mPLUG (Huge)Accuracy82.43—Unverified
6CuMo-7BAccuracy82.2—Unverified
7X2-VLM (large)Accuracy81.9—Unverified
8MMUAccuracy81.26—Unverified
9LyricsAccuracy81.2—Unverified
10InternVL-CAccuracy81.2—Unverified
#ModelMetricClaimedVerifiedStatus
1BEiT-3overall84.03—Unverified
2mPLUG-Hugeoverall83.62—Unverified
3ONE-PEACEoverall82.52—Unverified
4X2-VLM (large)overall81.8—Unverified
5VLMooverall81.3—Unverified
6SimVLMoverall80.34—Unverified
7X2-VLM (base)overall80.2—Unverified
8VASToverall80.19—Unverified
9VALORoverall78.62—Unverified
10Prompt Tuningoverall78.53—Unverified