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 1426–1450 of 2167 papers

TitleStatusHype
EmoAssist: Emotional Assistant for Visual Impairment Community—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation—0
Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories—0
Enforcing Reasoning in Visual Commonsense Reasoning—0
Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering—0
Enhanced Textual Feature Extraction for Visual Question Answering: A Simple Convolutional Approach—0
Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations—0
Enhancing Generalization in Medical Visual Question Answering Tasks via Gradient-Guided Model Perturbation—0
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy—0
Enhancing Multi-hop Reasoning in Vision-Language Models via Self-Distillation with Multi-Prompt Ensembling—0
Enhancing Multi-Image Question Answering via Submodular Subset Selection—0
Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference—0
Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion—0
Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering—0
Erasure for Advancing: Dynamic Self-Supervised Learning for Commonsense Reasoning—0
ErgoChat: a Visual Query System for the Ergonomic Risk Assessment of Construction Workers—0
ERNIE-UniX2: A Unified Cross-lingual Cross-modal Framework for Understanding and Generation—0
ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph—0
Estimating semantic structure for the VQA answer space—0
ESVQA: Perceptual Quality Assessment of Egocentric Spatial Videos—0
Evaluating Attribute Confusion in Fashion Text-to-Image Generation—0
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data—0
Evaluating the Representational Hub of Language and Vision Models—0
Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarks—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