2.2k citations · 5.2k across the 82 of their papers we have counts for
6 papers · 2 filters
Feature Generating Networks for Zero-Shot Learning
Yongqin Xian, Tobias Lorenz, Bernt Schiele +1
Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challe…
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (Extended Abstract)
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata +4
Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. On the other hand, their opaqueness has led to…
Grounding Visual Explanations (Extended Abstract)
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell +1
Existing models which generate textual explanations enforce task relevance through a discriminative term loss function, but such mechanisms only weakly constrain mentioned object p…
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Yongqin Xian, Christoph H. Lampert, Bernt Schiele +1
Due to the importance of zero-shot learning, i.e. classifying images where there is a lack of labeled training data, the number of proposed approaches has recently increased steadi…
Zero-Shot Learning -- The Good, the Bad and the Ugly
Yongqin Xian, Bernt Schiele, Zeynep Akata
Due to the importance of zero-shot learning, the number of proposed approaches has increased steadily recently. We argue that it is time to take a step back and to analyze the stat…
Exploiting saliency for object segmentation from image level labels
Seong Joon Oh, Rodrigo Benenson, Anna Khoreva +3
There have been remarkable improvements in the semantic labelling task in the recent years. However, the state of the art methods rely on large-scale pixel-level annotations. This…