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 1276–1300 of 2167 papers

TitleStatusHype
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
Pre-training image-language transformers for open-vocabulary tasks—0
Priorformer: A UGC-VQA Method with content and distortion priors—0
Privacy-Aware Visual Language Models—0
Privacy Preserving Visual Question Answering—0
PRNet: A Progressive Regression Network for No-Reference User-Generated-Content Video Quality Assessment—0
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering—0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training—0
Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training—0
Probing the Role of Positional Information in Vision-Language Models—0
Probing Visual Language Priors in VLMs—0
ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data—0
Progressive Attention Memory Network for Movie Story Question Answering—0
Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning—0
Prompt-based Personalized Federated Learning for Medical Visual Question Answering—0
PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3—0
Prompting Large Language Models with Rationale Heuristics for Knowledge-based Visual Question Answering—0
Prompt Tuning for Generative Multimodal Pretrained 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