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 1151–1175 of 2167 papers

TitleStatusHype
RSAdapter: Adapting Multimodal Models for Remote Sensing Visual Question AnsweringCode0
UNK-VQA: A Dataset and a Probe into the Abstention Ability of Multi-modal Large ModelsCode0
Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA—0
Open-Set Knowledge-Based Visual Question Answering with Inference PathsCode0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model—0
Off-Policy Evaluation for Human Feedback—0
Improving mitosis detection on histopathology images using large vision-language models—0
How (not) to ensemble LVLMs for VQA—0
Causal Reasoning through Two Layers of Cognition for Improving Generalization in Visual Question Answering—0
Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models—0
Improving Automatic VQA Evaluation Using Large Language Models—0
On the Cognition of Visual Question Answering Models and Human Intelligence: A Comparative Study—0
SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering—0
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image ModelsCode0
ELIP: Efficient Language-Image Pre-training with Fewer Vision TokensCode0
Tackling VQA with Pretrained Foundation Models without Further Training—0
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and CompositionCode0
Implicit Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Sentence Attention Blocks for Answer Grounding—0
Visual Question Answering in the Medical Domain—0
Syntax Tree Constrained Graph Network for Visual Question Answering—0
D3: Data Diversity Design for Systematic Generalization in Visual Question AnsweringCode0
Interpretable Visual Question Answering via Reasoning Supervision—0
S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning—0
Distraction-free Embeddings for Robust 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
9InternVL-CAccuracy81.2—Unverified
10LyricsAccuracy81.2—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