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
Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects—0
Adversarial Representation Learning for Text-to-Image Matching—0
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models—0
Aesthetic Visual Question Answering of Photographs—0
A Focused Dynamic Attention Model for Visual Question Answering—0
A Framework to Map VMAF with the Probability of Just Noticeable Difference between Video Encoding Recipes—0
A Free Lunch in Generating Datasets: Building a VQG and VQA System with Attention and Humans in the Loop—0
A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions—0
AGFSync: Leveraging AI-Generated Feedback for Preference Optimization in Text-to-Image Generation—0
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP—0
A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models—0
AI2D-RST: A multimodal corpus of 1000 primary school science diagrams—0
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering—0
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks—0
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models—0
Aligning MAGMA by Few-Shot Learning and Finetuning—0
Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering—0
AlignVE: Visual Entailment Recognition Based on Alignment Relations—0
All You May Need for VQA are Image Captions—0
``Look, some Green Circles!'': Learning to Quantify from Images—0
American == White in Multimodal Language-and-Image AI—0
A Multimodal Memes Classification: A Survey and Open Research Issues—0
Analysis of Visual Question Answering Algorithms with attention model—0
Analysis on Image Set Visual Question Answering—0
An Analysis of Visual Question Answering Algorithms—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