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 526–550 of 2167 papers

TitleStatusHype
VQA Training Sets are Self-play Environments for Generating Few-shot Pools—0
Instruction-Guided Visual MaskingCode1
Reverse Image Retrieval Cues Parametric Memory in Multimodal LLMsCode1
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
LM4LV: A Frozen Large Language Model for Low-level Vision TasksCode2
SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge—0
PitVQA: Image-grounded Text Embedding LLM for Visual Question Answering in Pituitary SurgeryCode1
Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question AnsweringCode0
MTVQA: Benchmarking Multilingual Text-Centric Visual Question AnsweringCode2
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions—0
EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging—0
StackOverflowVQA: Stack Overflow Visual Question Answering Dataset—0
RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content—0
Realizing Visual Question Answering for Education: GPT-4V as a Multimodal AI—0
Federated Document Visual Question Answering: A Pilot StudyCode0
Exploring the Capabilities of Large Multimodal Models on Dense TextCode4
CuMo: Scaling Multimodal LLM with Co-Upcycled Mixture-of-ExpertsCode2
Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering—0
VSA4VQA: Scaling a Vector Symbolic Architecture to Visual Question Answering on Natural Images—0
Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language GuidanceCode1
Advancing Multimodal Medical Capabilities of Gemini—0
OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual ReasoningCode4
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis—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