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SOTA
Visuelles Fragebeantworten (VQA)
Visual Question Answering On Clevr
Visual Question Answering On Clevr
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
NS-VQA (1K programs)
99.8
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
MDETR
99.7
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
NeSyCoCo
99.7
NeSyCoCo: A Neuro-Symbolic Concept Composer for Compositional Generalization
OCCAM (ours)
99.4
Interpretable Visual Reasoning via Induced Symbolic Space
TbD + reg + hres
99.1
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
MAC
98.9
Compositional Attention Networks for Machine Reasoning
NS-CL
98.9
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
CNN + LSTM + RN + HAN
98.8
Learning Visual Question Answering by Bootstrapping Hard Attention
DDRprog*
98.3
DDRprog: A CLEVR Differentiable Dynamic Reasoning Programmer
single-hop + LCGN (ours)
97.9
Language-Conditioned Graph Networks for Relational Reasoning
XNM-Det supervised
97.7
Explainable and Explicit Visual Reasoning over Scene Graphs
CNN+GRU+FiLM
97.7
FiLM: Visual Reasoning with a General Conditioning Layer
IEP-700K
96.9
Inferring and Executing Programs for Visual Reasoning
CNN + LSTM + RN
95.50
A simple neural network module for relational reasoning
QGHC+Att+Concat
65.90
Question-Guided Hybrid Convolution for Visual Question Answering
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Visual Question Answering On Clevr | SOTA | HyperAI