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 476–500 of 2167 papers

TitleStatusHype
MiniGPT-Med: Large Language Model as a General Interface for Radiology DiagnosisCode2
Visual Robustness Benchmark for Visual Question Answering (VQA)Code0
MindBench: A Comprehensive Benchmark for Mind Map Structure Recognition and Analysis—0
BACON: Improving Clarity of Image Captions via Bag-of-Concept Graphs—0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions—0
A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document UnderstandingCode2
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness—0
https://arxiv.org/abs/2407.00634Code0
μ-Bench: A Vision-Language Benchmark for Microscopy UnderstandingCode0
Hierarchical Memory for Long Video QA—0
Tarsier: Recipes for Training and Evaluating Large Video Description ModelsCode4
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data SynthesisCode1
STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-AnsweringCode1
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs—0
Disentangling Knowledge-based and Visual Reasoning by Question Decomposition in KB-VQA—0
Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature DistillationCode0
RAVEN: Multitask Retrieval Augmented Vision-Language Learning—0
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at ScaleCode3
On the Role of Visual Grounding in VQA—0
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs—0
Long Context Transfer from Language to VisionCode4
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
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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