622 citations · 1.4k across the 32 of their papers we have counts for
6 papers · 1 filter
Self-Supervised Relative Depth Learning for Urban Scene Understanding
Huaizu Jiang, Erik Learned-Miller, Gustav Larsson +2
As an agent moves through the world, the apparent motion of scene elements is (usually) inversely proportional to their depth. It is natural for a learning agent to associate image…
Semantic speech retrieval with a visually grounded model of untranscribed speech
Herman Kamper, Gregory Shakhnarovich, Karen Livescu
There is growing interest in models that can learn from unlabelled speech paired with visual context. This setting is relevant for low-resource speech processing, robotics, and hum…
Training Deep Networks to be Spatially Sensitive
Nicholas Kolkin, Gregory Shakhnarovich, Eli Shechtman
In many computer vision tasks, for example saliency prediction or semantic segmentation, the desired output is a foreground map that predicts pixels where some criteria is satisfie…
Visually grounded learning of keyword prediction from untranscribed speech
Herman Kamper, Shane Settle, Gregory Shakhnarovich +1
During language acquisition, infants have the benefit of visual cues to ground spoken language. Robots similarly have access to audio and visual sensors. Recent work has shown that…
Colorization as a Proxy Task for Visual Understanding
Gustav Larsson, Michael Maire, Gregory Shakhnarovich
We investigate and improve self-supervision as a drop-in replacement for ImageNet pretraining, focusing on automatic colorization as the proxy task. Self-supervised training has be…
Comprehension-guided referring expressions
Ruotian Luo, Gregory Shakhnarovich
We consider generation and comprehension of natural language referring expression for objects in an image. Unlike generic "image captioning" which lacks natural standard evaluation…