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 1351–1375 of 2167 papers

TitleStatusHype
VrR-VG: Refocusing Visually-Relevant Relationships—0
Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering—0
Retrieval-Augmented Visual Question Answering via Built-in Autoregressive Search Engines—0
Retrieving Visual Facts For Few-Shot Visual Question Answering—0
Review of Ansatz Designing Techniques for Variational Quantum Algorithms—0
Revisiting Multi-Modal LLM Evaluation—0
ReWind: Understanding Long Videos with Instructed Learnable Memory—0
ReXVQA: A Large-scale Visual Question Answering Benchmark for Generalist Chest X-ray Understanding—0
RL-CSDia: Representation Learning of Computer Science Diagrams—0
R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest—0
RMLVQA: A Margin Loss Approach for Visual Question Answering With Language Biases—0
RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content—0
Robo2VLM: Visual Question Answering from Large-Scale In-the-Wild Robot Manipulation Datasets—0
Robustness Analysis of Visual QA Models by Basic Questions—0
Robusto-1 Dataset: Comparing Humans and VLMs on real out-of-distribution Autonomous Driving VQA from Peru—0
Robust Visual Question Answering: Datasets, Methods, and Future Challenges—0
Robust Visual Reasoning via Language Guided Neural Module Networks—0
RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data—0
RSVQA: Visual Question Answering for Remote Sensing Data—0
S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning—0
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning—0
SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering—0
SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement—0
Scaling Large Vision-Language Models for Enhanced Multimodal Comprehension In Biomedical Image Analysis—0
Scallop: From Probabilistic Deductive Databases to Scalable Differentiable 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