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 501–525 of 2167 papers

TitleStatusHype
LIVE: Learnable In-Context Vector for Visual Question AnsweringCode1
Biomedical Visual Instruction Tuning with Clinician Preference AlignmentCode0
Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQACode0
MMNeuron: Discovering Neuron-Level Domain-Specific Interpretation in Multimodal Large Language ModelCode1
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food CultureCode1
AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language ModelsCode3
Beyond Raw Videos: Understanding Edited Videos with Large Multimodal ModelCode0
What is the Visual Cognition Gap between Humans and Multimodal LLMs?Code0
Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models—0
Vision-Language Models Meet Meteorology: Developing Models for Extreme Weather Events Detection with HeatmapsCode1
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns—0
VisionLLM v2: An End-to-End Generalist Multimodal Large Language Model for Hundreds of Vision-Language TasksCode5
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMsCode5
CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark—0
Composition Vision-Language Understanding via Segment and Depth Anything ModelCode0
Understanding Information Storage and Transfer in Multi-modal Large Language Models—0
Diffusion-Refined VQA Annotations for Semi-Supervised Gaze FollowingCode0
Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering—0
Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language ModelsCode2
Selectively Answering Visual Questions—0
TabPedia: Towards Comprehensive Visual Table Understanding with Concept SynergyCode2
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering—0
DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language ModelsCode2
Ovis: Structural Embedding Alignment for Multimodal Large Language ModelCode5
VQA Training Sets are Self-play Environments for Generating Few-shot Pools—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