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 201–225 of 2167 papers

TitleStatusHype
Visual Graph Question Answering with ASP and LLMs for Language Parsing—0
Abduction of Domain Relationships from Data for VQA—0
ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical ImagesCode0
Performance Analysis of Traditional VQA Models Under Limited Computational Resources—0
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment—0
Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware ConsistencyCode1
No Images, No Problem: Retaining Knowledge in Continual VQA with Questions-Only MemoryCode0
Efficient Few-Shot Continual Learning in Vision-Language Models—0
HD-EPIC: A Highly-Detailed Egocentric Video Dataset—0
Variational Quantum Optimization with Continuous BanditsCode0
Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language ModelsCode1
VLM-Assisted Continual learning for Visual Question Answering in Self-Driving—0
Hypo3D: Exploring Hypothetical Reasoning in 3D—0
Large Models in Dialogue for Active Perception and Anomaly DetectionCode0
Scaling Large Vision-Language Models for Enhanced Multimodal Comprehension In Biomedical Image Analysis—0
Scene Understanding Enabled Semantic Communication with Open Channel Coding—0
Patent Figure Classification using Large Vision-language ModelsCode0
Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual Question Answering—0
Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No!—0
Embodied Scene Understanding for Vision Language Models via MetaVQA—0
Cross-Modal Transferable Image-to-Video Attack on Video Quality MetricsCode0
Video Quality Assessment for Online Processing: From Spatial to Temporal Sampling—0
The Quest for Visual Understanding: A Journey Through the Evolution of Visual Question Answering—0
Overcoming Language Priors for Visual Question Answering Based on Knowledge Distillation—0
Commonsense Video Question Answering through Video-Grounded Entailment Tree Reasoning—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