105 citations · 436 across the 29 of their papers we have counts for
7 papers · 1 filter
Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations
Mohammadreza Salehi, Efstratios Gavves, Cees G. M. Snoek +1
Spatially dense self-supervised learning is a rapidly growing problem domain with promising applications for unsupervised segmentation and pretraining for dense downstream tasks. D…
Efficient Neural PDE-Solvers using Quantization Aware Training
Winfried van den Dool, Tijmen Blankevoort, Max Welling +1
In the past years, the application of neural networks as an alternative to classical numerical methods to solve Partial Differential Equations has emerged as a potential paradigm s…
Learning to Count without Annotations
Lukas Knobel, Tengda Han, Yuki M. Asano
While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost a…
BISCUIT: Causal Representation Learning from Binary Interactions
Phillip Lippe, Sara Magliacane, Sindy Löwe +3
Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and embodied AI. While an agent can commonly inter…
Self-Ordering Point Clouds
Pengwan Yang, Cees G. M. Snoek, Yuki M. Asano
In this paper we address the task of finding representative subsets of points in a 3D point cloud by means of a point-wise ordering. Only a few works have tried to address this cha…
Towards Label-Efficient Incremental Learning: A Survey
Mert Kilickaya, Joost van de Weijer, Yuki M. Asano
The current dominant paradigm when building a machine learning model is to iterate over a dataset over and over until convergence. Such an approach is non-incremental, as it assume…
Skip-Attention: Improving Vision Transformers by Paying Less Attention
Shashanka Venkataramanan, Amir Ghodrati, Yuki M. Asano +2
This work aims to improve the efficiency of vision transformers (ViT). While ViTs use computationally expensive self-attention operations in every layer, we identify that these ope…