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 1176–1200 of 2167 papers

TitleStatusHype
Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues—0
Learning Rich Image Region Representation for Visual Question Answering—0
Learning Sparse Mixture of Experts for Visual Question Answering—0
Learning to Answer Multilingual and Code-Mixed Questions—0
Learning to Answer Questions From Image Using Convolutional Neural Network—0
Learning to Collocate Neural Modules for Image Captioning—0
Learning to Compose Diversified Prompts for Image Emotion Classification—0
Learning to Compress Contexts for Efficient Knowledge-based Visual Question Answering—0
Learning to Disambiguate by Asking Discriminative Questions—0
Learning to Reason Iteratively and Parallelly for Complex Visual Reasoning Scenarios—0
Neural Reasoning, Fast and Slow, for Video Question Answering—0
Learning to Recognize the Unseen Visual Predicates—0
Learning to Select Question-Relevant Relations for Visual Question Answering—0
Learning to Specialize with Knowledge Distillation for Visual Question Answering—0
Learning Visual Knowledge Memory Networks for Visual Question Answering—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?—0
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model—0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Leveraging Video Descriptions to Learn Video Question Answering—0
Leveraging Visual Question Answering for Image-Caption Ranking—0
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis—0
Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning—0
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation—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