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 1701–1725 of 2167 papers

TitleStatusHype
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering—0
Visual Question Answering on Image Sets—0
No-Reference Video Quality Assessment Using Space-Time ChipsCode0
Document Visual Question Answering Challenge 2020—0
Linguistically-aware Attention for Reducing the Semantic-Gap in Vision-Language Tasks—0
Graph Edit Distance Reward: Learning to Edit Scene Graph—0
Assisting Scene Graph Generation with Self-Supervision—0
TRRNet: Tiered Relation Reasoning for Compositional Visual Question Answering—0
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision—0
Noise-Induced Barren Plateaus in Variational Quantum AlgorithmsCode0
REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question AnsweringCode0
Contrastive Visual-Linguistic PretrainingCode0
Dialog without Dialog Data: Learning Visual Dialog Agents from VQA DataCode0
Reducing Language Biases in Visual Question Answering with Visually-Grounded Question Encoder—0
Applying recent advances in Visual Question Answering to Record LinkageCode0
Image Captioning with Compositional Neural Module Networks—0
IQ-VQA: Intelligent Visual Question AnsweringCode0
Eliminating Catastrophic Interference with Biased Competition—0
Visual Question Answering as a Multi-Task Problem—0
Scene Graph Reasoning for Visual Question Answering—0
The Impact of Explanations on AI Competency Prediction in VQA—0
Multimodal Neural Graph Memory Networks for Visual Question Answering—0
Towards Visual Dialog for Radiology—0
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering—0
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph—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