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 251–275 of 2167 papers

TitleStatusHype
ErgoChat: a Visual Query System for the Ergonomic Risk Assessment of Construction Workers—0
FineVQ: Fine-Grained User Generated Content Video Quality Assessment—0
Multi-Agents Based on Large Language Models for Knowledge-based Visual Question Answering—0
EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model EvaluationCode2
HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images—0
TextMatch: Enhancing Image-Text Consistency Through Multimodal Optimization—0
LININ: Logic Integrated Neural Inference Network for Explanatory Visual Question AnsweringCode0
Cross-Lingual Text-Rich Visual Comprehension: An Information Theory PerspectiveCode0
Prompting Large Language Models with Rationale Heuristics for Knowledge-based Visual Question Answering—0
Application of Multimodal Large Language Models in Autonomous Driving—0
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional GeneralizationCode0
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage—0
InstructOCR: Instruction Boosting Scene Text SpottingCode0
Multimodal Hypothetical Summary for Retrieval-based Multi-image Question AnsweringCode0
OnlineVPO: Align Video Diffusion Model with Online Video-Centric Preference Optimization—0
MedCoT: Medical Chain of Thought via Hierarchical ExpertCode1
What makes a good metric? Evaluating automatic metrics for text-to-image consistency—0
Optimizing Vision-Language Interactions Through Decoder-Only Models—0
Selective State Space Memory for Large Vision-Language Models—0
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation—0
Towards a Multimodal Large Language Model with Pixel-Level Insight for BiomedicineCode2
Lyra: An Efficient and Speech-Centric Framework for Omni-CognitionCode3
Fast Prompt Alignment for Text-to-Image GenerationCode1
Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual IllusionsCode0
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