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 801–825 of 2167 papers

TitleStatusHype
How Well Can Vison-Language Models Understand Humans' Intention? An Open-ended Theory of Mind Question Evaluation Benchmark—0
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment—0
Language Features Matter: Effective Language Representations for Vision-Language Tasks—0
CQ-VQA: Visual Question Answering on Categorized Questions—0
Compressing Visual-linguistic Model via Knowledge Distillation—0
Grounded Word Sense Translation—0
Knowledge Condensation and Reasoning for Knowledge-based VQA—0
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?—0
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment—0
HVS Revisited: A Comprehensive Video Quality Assessment Framework—0
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models—0
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering—0
Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering—0
Graph-Structured Representations for Visual Question Answering—0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
Graph Relation Transformer: Incorporating pairwise object features into the Transformer architecture—0
Bilinear Graph Networks for Visual Question Answering—0
ICDAR 2021 Competition on Document VisualQuestion Answering—0
Aligning MAGMA by Few-Shot Learning and Finetuning—0
Graph Neural Networks in Vision-Language Image Understanding: A Survey—0
Compositional Memory for Visual Question Answering—0
Learning Compositional Representation for Few-shot Visual Question Answering—0
Graph Edit Distance Reward: Learning to Edit Scene Graph—0
A survey on VQA_Datasets and Approaches—0
A survey on knowledge-enhanced multimodal learning—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