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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1226–1250 of 2167 papers

TitleStatusHype
Making Video Quality Assessment Models Sensitive to Frame Rate Distortions—0
Making Video Quality Assessment Models Robust to Bit Depth—0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
MANGO: Enhancing the Robustness of VQA Models via Adversarial Noise Generation—0
Mask4Align: Aligned Entity Prompting with Color Masks for Multi-Entity Localization Problems—0
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering—0
Measuring CLEVRness: Black-box Testing of Visual Reasoning Models—0
Measuring CLEVRness: Blackbox testing of Visual Reasoning Models—0
Measuring Machine Intelligence Through Visual Question Answering—0
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model—0
MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning—0
Medical Visual Question Answering: A Survey—0
Medical visual question answering using joint self-supervised learning—0
MedSG-Bench: A Benchmark for Medical Image Sequences Grounding—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
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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