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 1326–1350 of 2167 papers

TitleStatusHype
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks—0
CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering—0
CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answering—0
CoBIT: A Contrastive Bi-directional Image-Text Generation Model—0
COCO is "ALL'' You Need for Visual Instruction Fine-tuning—0
COIN: Counterfactual Image Generation for VQA Interpretation—0
Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering—0
Evaluating and Improving Interactions with Hazy Oracles—0
ComicsPAP: understanding comic strips by picking the correct panel—0
Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning—0
Compact Tensor Pooling for Visual Question Answering—0
Component Analysis for Visual Question Answering Architectures—0
Compositional Attention Networks for Interpretability in Natural Language Question Answering—0
Compositional Memory for Visual Question Answering—0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
Compressing Visual-linguistic Model via Knowledge Distillation—0
Connecting Language and Vision to Actions—0
Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms—0
Convolutional Neural Networks for Video Quality Assessment—0
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG—0
Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits—0
Counterfactual Vision and Language Learning—0
Co-VQA : Answering by Interactive Sub Question Sequence—0
Co-VQA : Answering by Interactive Sub Question Sequence—0
CP-LLM: Context and Pixel Aware Large Language Model for Video Quality Assessment—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