Visual Reasoning
85 papers with code • 7 benchmarks • 25 datasets
Ability to understand actions and reasoning associated with any visual images
Libraries
Use these libraries to find Visual Reasoning models and implementations
RefCOCO
SHAPES
AbstractReasoning
NLVR
SNLI-VE
RAVEN
PGM
CATER
PHYRE
Most implemented papers
Compositional Attention Networks for Machine Reasoning
stanfordnlp/mac-network
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ICLR 2018
We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning.
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
airsplay/lxmert
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IJCNLP 2019
In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder.
Inferring and Executing Programs for Visual Reasoning
facebookresearch/clevr-iep
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ICCV 2017
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.
Learning to Compose Dynamic Tree Structures for Visual Contexts
KaihuaTang/Scene-Graph-Benchmark.pytorch
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CVPR 2019
We propose to compose dynamic tree structures that place the objects in an image into a visual context, helping visual reasoning tasks such as scene graph generation and visual Q&A.
VisualBERT: A Simple and Performant Baseline for Vision and Language
We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks.
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
ethanjperez/film
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CVPR 2017
When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings.
FiLM: Visual Reasoning with a General Conditioning Layer
ethanjperez/film
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We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation.
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
stanfordnlp/mac-network
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CVPR 2019
We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets.
CLEVR-Ref+: Diagnosing Visual Reasoning with Referring Expressions
ruotianluo/iep-ref
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CVPR 2019
Yet there has been evidence that current benchmark datasets suffer from bias, and current state-of-the-art models cannot be easily evaluated on their intermediate reasoning process.
Learning by Abstraction: The Neural State Machine
stanfordnlp/mac-network
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NeurIPS 2019
We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reasoning.
