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 1201–1225 of 2167 papers

TitleStatusHype
StarVQA+: Co-training Space-Time Attention for Video Quality AssessmentCode0
Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories—0
AVIS: Autonomous Visual Information Seeking with Large Language Model Agent—0
Visual Question Answering (VQA) on Images with Superimposed Text—0
Weakly Supervised Visual Question Answer Generation—0
Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering—0
Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes—0
MetaVL: Transferring In-Context Learning Ability From Language Models to Vision-Language Models—0
Overcoming Language Bias in Remote Sensing Visual Question Answering via Adversarial Training—0
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data—0
LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing—0
Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models—0
Unveiling Cross Modality Bias in Visual Question Answering: A Causal View with Possible Worlds VQA—0
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge—0
HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa LanguageCode0
Modularized Zero-shot VQA with Pre-trained ModelsCode0
Zero-shot Visual Question Answering with Language Model FeedbackCode0
Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos—0
Dynamic Clue Bottlenecks: Towards Interpretable-by-Design Visual Question Answering—0
Measuring Faithful and Plausible Visual Grounding in VQACode0
Transferring Visual Attributes from Natural Language to Verified Image Generation—0
Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach—0
DUBLIN -- Document Understanding By Language-Image Network—0
Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design—0
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending—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