Benchmarks
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Subtasks
Datasets
Greatest papers with code
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
IJCNLP 2019
• huggingface/transformers
• 
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.
Ranked #1 on
Visual Question Answering
on VizWiz 2018
LANGUAGE MODELLING QUESTION ANSWERING VISUAL QUESTION ANSWERING VISUAL REASONING
Inferring and Executing Programs for Visual Reasoning
ICCV 2017
• facebookresearch/clevr-iep
•
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.
Ranked #4 on
Visual Question Answering
on CLEVR-Humans
Learning to Compose Dynamic Tree Structures for Visual Contexts
CVPR 2019
• KaihuaTang/Scene-Graph-Benchmark.pytorch
•
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.
Ranked #4 on
Scene Graph Generation
on Visual Genome
GRAPH GENERATION SCENE GRAPH GENERATION VISUAL QUESTION ANSWERING VISUAL REASONING
Learning by Abstraction: The Neural State Machine
NeurIPS 2019
• stanfordnlp/mac-network
• 
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.
Ranked #1 on
Visual Question Answering
on GQA test-dev
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
CVPR 2019
• stanfordnlp/mac-network
•
We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets.
Ranked #4 on
Visual Question Answering
on GQA test-std
QUESTION ANSWERING VISUAL QUESTION ANSWERING VISUAL REASONING
Compositional Attention Networks for Machine Reasoning
ICLR 2018
• stanfordnlp/mac-network
•
We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning.
Ranked #1 on
Visual Question Answering
on CLEVR-Humans
PHYRE: A New Benchmark for Physical Reasoning
The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles.
Ranked #3 on
Visual Reasoning
on PHYRE-1B-Within
Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning
CVPR 2018
• davidmascharka/tbd-nets
•
Recently, modular networks have been shown to be an effective framework for performing visual reasoning tasks.
Ranked #3 on
Visual Question Answering
on CLEVR
QUESTION ANSWERING VISUAL QUESTION ANSWERING VISUAL REASONING
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.
Ranked #1 on
Phrase Grounding
on Flickr30k Entities Dev
LANGUAGE MODELLING VISUAL QUESTION ANSWERING VISUAL REASONING
A Corpus for Reasoning About Natural Language Grounded in Photographs
We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language.