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 1851–1875 of 2167 papers

TitleStatusHype
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training—0
Overcoming Language Priors for Visual Question Answering Based on Knowledge Distillation—0
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization—0
OVQA: A Clinically Generated Visual Question Answering Dataset—0
PaLI: A Jointly-Scaled Multilingual Language-Image Model—0
PaLM2-VAdapter: Progressively Aligned Language Model Makes a Strong Vision-language Adapter—0
PAM: Understanding Product Images in Cross Product Category Attribute Extraction—0
NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms—0
Parameter-Parallel Distributed Variational Quantum Algorithm—0
ParsVQA-Caps: A Benchmark for Visual Question Answering and Image Captioning in Persian—0
Pathological Visual Question Answering—0
PathVLM-R1: A Reinforcement Learning-Driven Reasoning Model for Pathology Visual-Language Tasks—0
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering—0
PDFVQA: A New Dataset for Real-World VQA on PDF Documents—0
Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmark—0
Perceptual Quality Assessment of UGC Gaming Videos—0
Performance Analysis of Traditional VQA Models Under Limited Computational Resources—0
PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly—0
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals—0
PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models—0
Playing Lottery Tickets with Vision and Language—0
Polar-VQA: Visual Question Answering on Remote Sensed Ice sheet Imagery from Polar Region—0
Precision Empowers, Excess Distracts: Visual Question Answering With Dynamically Infused Knowledge In Language Models—0
Predicting Relative Depth between Objects from Semantic Features—0
PreSTU: Pre-Training for Scene-Text Understanding—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