2.7k citations · 3k across the 33 of their papers we have counts for
29 papers · 1 filter
Which Tokens to Use? Investigating Token Reduction in Vision Transformers
Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor +1
Since the introduction of the Vision Transformer (ViT), researchers have sought to make ViTs more efficient by removing redundant information in the processed tokens. While differe…
A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset
Zahra Gharaee, ZeMing Gong, Nicholas Pellegrino +13
In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-Insect Dataset. Each record is taxonomically classified by a…
Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers
Cong Wei, Brendan Duke, Ruowei Jiang +3
Vision Transformers (ViT) have shown their competitive advantages performance-wise compared to convolutional neural networks (CNNs) though they often come with high computational c…
GCNet: Probing Self-Similarity Learning for Generalized Counting Network
Mingjie Wang, Yande Li, Jun Zhou +2
The class-agnostic counting (CAC) problem has caught increasing attention recently due to its wide societal applications and arduous challenges. To count objects of different categ…
Understanding the impact of image and input resolution on deep digital pathology patch classifiers
Eu Wern Teh, Graham W. Taylor
We consider annotation efficient learning in Digital Pathology (DP), where expert annotations are expensive and thus scarce. We explore the impact of image and input resolution on…
Unconstrained Scene Generation with Locally Conditioned Radiance Fields
Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava +2
We tackle the challenge of learning a distribution over complex, realistic, indoor scenes. In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose sc…