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 1276–1300 of 2167 papers

TitleStatusHype
Beyond VQA: Generating Multi-word Answer and Rationale to Visual Questions—0
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations—0
BloomVQA: Assessing Hierarchical Multi-modal Comprehension—0
BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining—0
BRAVE: Broadening the visual encoding of vision-language models—0
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images—0
Breaking Down Questions for Outside-Knowledge VQA—0
Breaking Down Questions for Outside-Knowledge Visual Question Answering—0
Bridge Damage Cause Estimation Using Multiple Images Based on Visual Question Answering—0
Bridging the Gap between Recognition-level Pre-training and Commonsensical Vision-language Tasks—0
Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks—0
Bridging the Semantic Gaps: Improving Medical VQA Consistency with LLM-Augmented Question Sets—0
Bridging Video Quality Scoring and Justification via Large Multimodal Models—0
Bring Remote Sensing Object Detect Into Nature Language Model: Using SFT Method—0
BuDDIE: A Business Document Dataset for Multi-task Information Extraction—0
Building Trustworthy Multimodal AI: A Review of Fairness, Transparency, and Ethics in Vision-Language Tasks—0
C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network—0
CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model—0
Can Common VLMs Rival Medical VLMs? Evaluation and Strategic Insights—0
Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!—0
Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?—0
Can Pre-training help VQA with Lexical Variations?—0
Can SAR improve RSVQA performance?—0
Can Visual Language Models Replace OCR-Based Visual Question Answering Pipelines in Production? A Case Study in Retail—0
Can We Generate Visual Programs Without Prompting LLMs?—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