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 976–1000 of 2167 papers

TitleStatusHype
RAVEN: Multitask Retrieval Augmented Vision-Language Learning—0
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature DistillationCode0
On the Role of Visual Grounding in VQA—0
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs—0
Priorformer: A UGC-VQA Method with content and distortion priors—0
Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts—0
Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis—0
VGA: Vision GUI Assistant -- Minimizing Hallucinations through Image-Centric Fine-TuningCode0
Biomedical Visual Instruction Tuning with Clinician Preference AlignmentCode0
Diversify, Rationalize, and Combine: Ensembling Multiple QA Strategies for Zero-shot Knowledge-based VQACode0
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
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns—0
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
Mixture of Rationale: Multi-Modal Reasoning Mixture for Visual Question Answering—0
Selectively Answering Visual Questions—0
VQA Training Sets are Self-play Environments for Generating Few-shot Pools—0
Evaluating Zero-Shot GPT-4V Performance on 3D Visual Question Answering Benchmarks—0
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild—0
Privacy-Aware Visual Language Models—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