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 1826–1850 of 2167 papers

TitleStatusHype
OmniCount: Multi-label Object Counting with Semantic-Geometric Priors—0
Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts—0
OmniVL:One Foundation Model for Image-Language and Video-Language Tasks—0
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization—0
One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering—0
On Incorporating Semantic Prior Knowlegde in Deep Learning Through Embedding-Space Constraints—0
On Incorporating Semantic Prior Knowledge in Deep Learning Through Embedding-Space Constraints—0
OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization—0
On the Cognition of Visual Question Answering Models and Human Intelligence: A Comparative Study—0
On the Effects of Video Grounding on Language Models—0
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering—0
On the Flip Side: Identifying Counterexamples in Visual Question Answering—0
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering—0
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks—0
On the Role of Visual Grounding in VQA—0
On the Significance of Question Encoder Sequence Model in the Out-of-Distribution Performance in Visual Question Answering—0
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law—0
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation—0
OptiBox: Breaking the Limits of Proposals for Visual Grounding—0
Optimizing Explanations by Network Canonization and Hyperparameter Search—0
Optimizing Vision-Language Interactions Through Decoder-Only Models—0
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns—0
ORD: Object Relationship Discovery for Visual Dialogue Generation—0
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation—0
Out of the Box: Reasoning with Graph Convolution Nets for Factual 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