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 1776–1800 of 2167 papers

TitleStatusHype
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