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Found 24 papers, 14 papers with code
Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing
White balance (WB) algorithms in many commercial cameras assume single and uniform illumination, leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene.
Learning Symmetrization for Equivariance with Orbit Distance Minimization
We present a general framework for symmetrizing an arbitrary neural-network architecture and making it equivariant with respect to a given group.
Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning
The binding problem in artificial neural networks is actively explored with the goal of achieving human-level recognition skills through the comprehension of the world in terms of symbol-like entities.
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Pretraining molecular representations from large unlabeled data is essential for molecular property prediction due to the high cost of obtaining ground-truth labels.
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
In contrary to equivariant architectures, we use an arbitrary base model such as an MLP or a transformer and symmetrize it to be equivariant to the given group by employing a small equivariant network that parameterizes the probabilistic distribution underlying the symmetrization.
Ranked #1 on
Link Prediction
on PCQM-Contact
(using extra training data)
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity
Meanwhile, the Sparrow simulator utilizes a 2D grid-based world, simplified kinematics, and conversion-free data flow to achieve a lightweight design.
Ranked #1 on
Atari Games
on Atari 2600 Pong
Shepherding Slots to Objects: Towards Stable and Robust Object-Centric Learning
Object-centric learning (OCL) aspires general and compositional understanding of scenes by representing a scene as a collection of object-centric representations.
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
We propose Visual Token Matching (VTM), a universal few-shot learner for arbitrary dense prediction tasks.
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
The forward and backward cost are thus linear to the number of edges, which each attention head can also choose flexibly based on the input.
Equivariant Hypergraph Neural Networks
Many problems in computer vision and machine learning can be cast as learning on hypergraphs that represent higher-order relations.
Pure Transformers are Powerful Graph Learners
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice.
Ranked #15 on
Graph Regression
on PCQM4Mv2-LSC
A Brand New Dance Partner: Music-Conditioned Pluralistic Dancing Controlled by Multiple Dance Genres
When coming up with phrases of movement, choreographers all have their habits as they are used to their skilled dance genres.
Ranked #5 on
Motion Synthesis
on FineDance
UBoCo: Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection
Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events.
UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection
Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events.
Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs
We present a generalization of Transformers to any-order permutation invariant data (sets, graphs, and hypergraphs).
Ranked #5 on
Graph Regression
on PCQM4M-LSC
(Validation MAE metric)
Toward Integrated Human-machine Intelligence for Civil Engineering: An Interdisciplinary Perspective
This paper will lay the foundation for identifying relevant studies to form a research roadmap to address the four knowledge gaps identified.
Winning the CVPR'2021 Kinetics-GEBD Challenge: Contrastive Learning Approach
Generic Event Boundary Detection (GEBD) is a newly introduced task that aims to detect "general" event boundaries that correspond to natural human perception.
Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs
We present a generalization of Transformers to any-order permutation invariant data (sets, graphs, and hypergraphs).
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales.
Ranked #3 on
Point Cloud Generation
on ShapeNet Car
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm Under Mixed Illumination
Images in our dataset are mostly captured with illuminants existing in the scene, and the ground truth illumination is computed by taking the difference between the images with different illumination combination.
"Near" Weighted Utilitarian Characterizations of Pareto Optima
We characterize Pareto optimality via "near" weighted utilitarian welfare maximization.
Weak Monotone Comparative Statics
We develop a theory of monotone comparative statics based on weak set order -- in short, weak monotone comparative statics -- and identify the enabling conditions in the context of individual choices, Pareto optimal choices% for a coalition of agents, Nash equilibria of games, and matching theory.
A Deep Cybersickness Predictor Based on Brain Signal Analysis for Virtual Reality Contents
Then, a convolutional neural network (CNN) is applied to encode the cognitive representation of the EEG spectrogram.
Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network
Incorporating spatio-temporal human visual perception into video quality assessment (VQA) remains a formidable issue.
