Visual Reasoning
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
Greatest papers with code
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
NeurIPS 2019
• google-research/disentanglement_lib
• 
A disentangled representation encodes information about the salient factors of variation in the data independently.
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.
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.
LXMERT: Learning Cross-Modality Encoder Representations from Transformers
IJCNLP 2019
• airsplay/lxmert
• 
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.
SOTA for Visual Reasoning on NLVR
LANGUAGE MODELLING QUESTION ANSWERING VISUAL QUESTION ANSWERING VISUAL REASONING
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.
SOTA for Visual Question Answering on VQA v2 (Percentage correct metric )
GRAPH GENERATION SCENE GRAPH GENERATION 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.
#2 best model for
Visual Reasoning
on NLVR
LANGUAGE MODELLING VISUAL QUESTION ANSWERING VISUAL REASONING
FiLM: Visual Reasoning with a General Conditioning Layer
• ethanjperez/film
•
We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation.
Inferring and Executing Programs for Visual Reasoning
ICCV 2017
• ethanjperez/film
•
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
CVPR 2017
• ethanjperez/film
•
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.
QUESTION ANSWERING VISUAL QUESTION ANSWERING VISUAL REASONING
Object Level Visual Reasoning in Videos
ECCV 2018
• fabienbaradel/object_level_visual_reasoning
•
Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context.
HUMAN ACTIVITY RECOGNITION OBJECT DETECTION VISUAL REASONING