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 2126–2150 of 2167 papers

TitleStatusHype
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base—0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning—0
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns—0
Understanding Attention for Vision-and-Language Tasks—0
Understanding in Artificial Intelligence—0
Understanding Information Storage and Transfer in Multi-modal Large Language Models—0
Understanding Knowledge Gaps in Visual Question Answering: Implications for Gap Identification and Testing—0
Understanding the Role of Scene Graphs in Visual Question Answering—0
UnICLAM:Contrastive Representation Learning with Adversarial Masking for Unified and Interpretable Medical Vision Question Answering—0
UniCode: Learning a Unified Codebook for Multimodal Large Language Models—0
Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training—0
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action—0
Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks—0
Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation—0
Unified Scene Representation and Reconstruction for 3D Large Language Models—0
Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training—0
UniRVQA: A Unified Framework for Retrieval-Augmented Vision Question Answering via Self-Reflective Joint Training—0
UNITER: Learning UNiversal Image-TExt Representations—0
Un jeu de données pour répondre à des questions visuelles à propos d’entités nommées en utilisant des bases de connaissances (ViQuAE, a Dataset for Knowledge-based Visual Question Answering about Named Entities)—0
Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario—0
Unshuffling Data for Improved Generalization—0
Unshuffling Data for Improved Generalization in Visual Question Answering—0
Unsupervised Keyword Extraction for Full-sentence VQA—0
Unsupervised Vision-and-Language Pre-training via Retrieval-based Multi-Granular Alignment—0
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA—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