17 citations · 24 across the 9 of their papers we have counts for
10 papers
PolyMaX: General Dense Prediction with Mask Transformer
Xuan Yang, Liangzhe Yuan, Kimberly Wilber +8
Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or r…
SANPO: A Scene Understanding, Accessibility and Human Navigation Dataset
Sagar M. Waghmare, Kimberly Wilber, Dave Hawkey +9
Vision is essential for human navigation. The World Health Organization (WHO) estimates that 43.3 million people were blind in 2020, and this number is projected to reach 61 millio…
Exploring Fine-Grained Audiovisual Categorization with the SSW60 Dataset
Grant Van Horn, Rui Qian, Kimberly Wilber +3
We present a new benchmark dataset, Sapsucker Woods 60 (SSW60), for advancing research on audiovisual fine-grained categorization. While our community has made great strides in fin…
On Label Granularity and Object Localization
Elijah Cole, Kimberly Wilber, Grant Van Horn +6
Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels. However, many objects can be labeled…
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…
Improving Calibration in Deep Metric Learning With Cross-Example Softmax
Andreas Veit, Kimberly Wilber
Modern image retrieval systems increasingly rely on the use of deep neural networks to learn embedding spaces in which distance encodes the relevance between a given query and imag…
Understanding Image Quality and Trust in Peer-to-Peer Marketplaces
Xiao Ma, Lina Mezghani, Kimberly Wilber +4
As any savvy online shopper knows, second-hand peer-to-peer marketplaces are filled with images of mixed quality. How does image quality impact marketplace outcomes, and can qualit…