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 751–775 of 2167 papers

TitleStatusHype
Is GPT-3 all you need for Visual Question Answering in Cultural Heritage?—0
It Takes Two to Tango: Towards Theory of AI's Mind—0
Attention Mechanism based Cognition-level Scene Understanding—0
Hardware-Friendly Static Quantization Method for Video Diffusion Transformers—0
HAMMR: HierArchical MultiModal React agents for generic VQA—0
Prompting Medical Large Vision-Language Models to Diagnose Pathologies by Visual Question Answering—0
iVQA: Inverse Visual Question Answering—0
Joint Image Captioning and Question Answering—0
JTD-UAV: MLLM-Enhanced Joint Tracking and Description Framework for Anti-UAV Systems—0
Knowing Where to Look? Analysis on Attention of Visual Question Answering System—0
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool—0
Attention Guided Semantic Relationship Parsing for Visual Question Answering—0
Inverse Visual Question Answering with Multi-Level Attentions—0
Interpretable Visual Question Answering via Reasoning Supervision—0
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision—0
Investigating Biases in Textual Entailment Datasets—0
Guiding Visual Question Generation—0
Guiding Visual Question Answering with Attention Priors—0
Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms—0
HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images—0
Connecting Language and Vision to Actions—0
HD-EPIC: A Highly-Detailed Egocentric Video Dataset—0
Guiding Medical Vision-Language Models with Explicit Visual Prompts: Framework Design and Comprehensive Exploration of Prompt Variations—0
HDR-ChipQA: No-Reference Quality Assessment on High Dynamic Range Videos—0
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021—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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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