580 citations · 3.8k across the 56 of their papers we have counts for
11 papers · 2 filters
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data
Lisa Anne Hendricks, Subhashini Venugopalan, Marcus Rohrbach +3
While recent deep neural network models have achieved promising results on the image captioning task, they rely largely on the availability of corpora with paired image and sentenc…
Natural Language Object Retrieval
Ronghang Hu, Huazhe Xu, Marcus Rohrbach +3
In this paper, we address the task of natural language object retrieval, to localize a target object within a given image based on a natural language query of the object. Natural l…
Grounding of Textual Phrases in Images by Reconstruction
Anna Rohrbach, Marcus Rohrbach, Ronghang Hu +2
Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual content is a challenging problem with many applications for human-computer interaction and image-text ref…
Neural Module Networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell +1
Visual question answering is fundamentally compositional in nature---a question like "where is the dog?" shares substructure with questions like "what color is the dog?" and "where…
Spatial Semantic Regularisation for Large Scale Object Detection
Damian Mrowca, Marcus Rohrbach, Judy Hoffman +3
Large scale object detection with thousands of classes introduces the problem of many contradicting false positive detections, which have to be suppressed. Class-independent non-ma…
The Long-Short Story of Movie Description
Anna Rohrbach, Marcus Rohrbach, Bernt Schiele
Generating descriptions for videos has many applications including assisting blind people and human-robot interaction. The recent advances in image captioning as well as the releas…