630 citations · 2.3k across the 46 of their papers we have counts for
3 papers · 1 filter
Fully Convolutional Networks for Semantic Segmentation
Evan Shelhamer, Jonathan Long, Trevor Darrell
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, impro…
Generating Visual Explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach +3
Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generall…
Segmentation from Natural Language Expressions
Ronghang Hu, Marcus Rohrbach, Trevor Darrell
In this paper we approach the novel problem of segmenting an image based on a natural language expression. This is different from traditional semantic segmentation over a predefine…