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 1376–1400 of 2167 papers

TitleStatusHype
Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning—0
SceneGATE: Scene-Graph based co-Attention networks for TExt visual question answering—0
Scene Graph Generation with Geometric Context—0
Scene Graph Reasoning for Visual Question Answering—0
A Comprehensive Survey of Scene Graphs: Generation and Application—0
Scene Understanding Enabled Semantic Communication with Open Channel Coding—0
Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning—0
SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering—0
Secure Video Quality Assessment Resisting Adversarial Attacks—0
SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors—0
Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework—0
Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings—0
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering—0
Segmentation-guided Attention for Visual Question Answering from Remote Sensing Images—0
Segmentation Guided Attention Networks for Visual Question Answering—0
Select2Plan: Training-Free ICL-Based Planning through VQA and Memory Retrieval—0
Selectively Answering Visual Questions—0
Selective State Space Memory for Large Vision-Language Models—0
SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering—0
Self-Segregating and Coordinated-Segregating Transformer for Focused Deep Multi-Modular Network for Visual Question Answering—0
WeaQA: Weak Supervision via Captions for Visual Question Answering—0
Semantic Aligned Multi-modal Transformer for Vision-LanguageUnderstanding: A Preliminary Study on Visual QA—0
Semantically-Aware Game Image Quality Assessment—0
Semantic-aware Modular Capsule Routing for Visual Question Answering—0
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks—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