research
∙
09/08/2023
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Pretraining molecular representations from large unlabeled data is essen...
share
research
∙
06/05/2023
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
We present a novel framework to overcome the limitations of equivariant ...
research
∙
03/27/2023
Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matching
Dense prediction tasks are a fundamental class of problems in computer v...
research
∙
03/20/2023
Towards End-to-End Generative Modeling of Long Videos with Memory-Efficient Bidirectional Transformers
Autoregressive transformers have shown remarkable success in video gener...
research
∙
10/27/2022
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
To overcome the quadratic cost of self-attention, recent works have prop...
research
∙
08/22/2022
Equivariant Hypergraph Neural Networks
Many problems in computer vision and machine learning can be cast as lea...
research
∙
08/11/2022
Diverse Generative Adversarial Perturbations on Attention Space for Transferable Adversarial Attacks
Adversarial attacks with improved transferability - the ability of an ad...
research
∙
07/06/2022
Pure Transformers are Powerful Graph Learners
We show that standard Transformers without graph-specific modifications ...
research
∙
05/03/2022
Learning Economic Indicators by Aggregating Multi-Level Geospatial Information
High-resolution daytime satellite imagery has become a promising source ...
research
∙
03/28/2022
Part-based Pseudo Label Refinement for Unsupervised Person Re-identification
Unsupervised person re-identification (re-ID) aims at learning discrimin...
research
∙
01/04/2022
Learning to Generate Novel Classes for Deep Metric Learning
Deep metric learning aims to learn an embedding space where the distance...
research
∙
10/30/2021
Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution
Audio super resolution aims to predict the missing high resolution compo...
research
∙
10/28/2021
Multi-Task Processes
Neural Processes (NPs) consider a task as a function realized from a sto...
research
∙
10/27/2021
Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs
We present a generalization of Transformers to any-order permutation inv...
research
∙
04/14/2021
Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction
Learning to predict the long-term future of video frames is notoriously ...
research
∙
03/29/2021
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Generative modeling of set-structured data, such as point clouds, requir...
research
∙
12/21/2020
Improving Unsupervised Image Clustering With Robust Learning
Unsupervised image clustering methods often introduce alternative object...
research
∙
12/08/2020
Variational Interaction Information Maximization for Cross-domain Disentanglement
Cross-domain disentanglement is the problem of learning representations ...
research
∙
01/13/2020
High-Fidelity Synthesis with Disentangled Representation
Learning disentangled representation of data without supervision is an i...
research
∙
01/25/2019
Diversity-Sensitive Conditional Generative Adversarial Networks
We propose a simple yet highly effective method that addresses the mode-...
research
∙
08/22/2018
Learning Hierarchical Semantic Image Manipulation through Structured Representations
Understanding, reasoning, and manipulating semantic concepts of images h...
research
∙
01/16/2018
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis
We propose a novel hierarchical approach for text-to-image synthesis by ...
research
∙
06/25/2017
Decomposing Motion and Content for Natural Video Sequence Prediction
We propose a deep neural network for the prediction of future frames in ...
research
∙
01/02/2017
Weakly Supervised Semantic Segmentation using Web-Crawled Videos
We propose a novel algorithm for weakly supervised semantic segmentation...
research
∙
12/24/2015
Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network
We propose a novel weakly-supervised semantic segmentation algorithm bas...
research
∙
06/16/2015
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
We propose a novel deep neural network architecture for semi-supervised ...
research
∙
02/24/2015