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 1226–1250 of 2167 papers

TitleStatusHype
Are we asking the right questions in MovieQA?—0
Are we pretraining it right? Digging deeper into visio-linguistic pretraining—0
Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension—0
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question—0
Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning—0
ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM—0
A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models—0
A Shared Task on Multimodal Machine Translation and Crosslingual Image Description—0
A Short Survey of Systematic Generalization—0
Asking More Informative Questions for Grounded Retrieval—0
Asking questions on handwritten document collections—0
Ask Me Anything: Free-form Visual Question Answering Based on Knowledge from External Sources—0
Assessing Image Quality Issues for Real-World Problems—0
Assessing the Robustness of Visual Question Answering Models—0
Assessing Visual Quality of Omnidirectional Videos—0
Assessment of Subjective and Objective Quality of Live Streaming Sports Videos—0
Assisting Scene Graph Generation with Self-Supervision—0
Astrea: A MOE-based Visual Understanding Model with Progressive Alignment—0
A Study on Multimodal and Interactive Explanations for Visual Question Answering—0
A survey on knowledge-enhanced multimodal learning—0
A survey on VQA_Datasets and Approaches—0
A Thousand Words Are Worth More Than a Picture: Natural Language-Centric Outside-Knowledge Visual Question Answering—0
A Token-level Text Image Foundation Model for Document Understanding—0
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021—0
Attention Guided Semantic Relationship Parsing for Visual Question Answering—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