17 citations · 24 across the 9 of their papers we have counts for
4 papers · 1 filter
Bridging the Gap Between Object Detection and User Intent via Query-Modulation
Marco Fornoni, Chaochao Yan, Liangchen Luo +5
When interacting with objects through cameras, or pictures, users often have a specific intent. For example, they may want to perform a visual search. With most object detection mo…
When Does Contrastive Visual Representation Learning Work?
Elijah Cole, Xuan Yang, Kimberly Wilber +2
Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification. While the particulars…
Benchmarking Representation Learning for Natural World Image Collections
Grant Van Horn, Elijah Cole, Sara Beery +3
Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label s…
On the Reproducibility of Neural Network Predictions
Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit +4
Standard training techniques for neural networks involve multiple sources of randomness, e.g., initialization, mini-batch ordering and in some cases data augmentation. Given that n…