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 1576–1600 of 2167 papers

TitleStatusHype
CLIPPO: Image-and-Language Understanding from Pixels Only—0
Image Captioning and Visual Question Answering Based on Attributes and External Knowledge—0
Image Captioning with Compositional Neural Module Networks—0
Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach—0
Image Position Prediction in Multimodal Documents—0
Image Semantic Relation Generation—0
ImageTTR: Grounding Type Theory with Records in Image Classification for Visual Question Answering—0
Improved Bilinear Pooling with CNNs—0
Improved Few-Shot Image Classification Through Multiple-Choice Questions—0
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection—0
Improving Automatic VQA Evaluation Using Large Language Models—0
Improving Cross-Modal Understanding in Visual Dialog via Contrastive Learning—0
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning—0
Improving Generalization in Visual Reasoning via Self-Ensemble—0
Improving Medical Reasoning with Curriculum-Aware Reinforcement Learning—0
Improving mitosis detection on histopathology images using large vision-language models—0
Improving Users' Mental Model with Attention-directed Counterfactual Edits—0
Improving Vision-and-Language Reasoning via Spatial Relations Modeling—0
Improving Visual Question Answering by Referring to Generated Paragraph Captions—0
Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions—0
Improving VQA and its Explanations \\ by Comparing Competing Explanations—0
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks—0
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering—0
InfographicVQA—0
Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space—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