2.2k citations · 5.2k across the 82 of their papers we have counts for
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Attentive Explanations: Justifying Decisions and Pointing to the Evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata +3
Deep models are the defacto standard in visual decision models due to their impressive performance on a wide array of visual tasks. However, they are frequently seen as opaque and…
Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
Levent Karacan, Zeynep Akata, Aykut Erdem +1
Automatic image synthesis research has been rapidly growing with deep networks getting more and more expressive. In the last couple of years, we have observed images of digits, ind…
Gaze Embeddings for Zero-Shot Image Classification
Nour Karessli, Zeynep Akata, Bernt Schiele +1
Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We…
Learning What and Where to Draw
Scott Reed, Zeynep Akata, Santosh Mohan +3
Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, bir…
Generative Adversarial Text to Image Synthesis
Scott Reed, Zeynep Akata, Xinchen Yan +3
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerfu…
Learning Deep Representations of Fine-grained Visual Descriptions
Scott Reed, Zeynep Akata, Bernt Schiele +1
State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best com…
Latent Embeddings for Zero-shot Classification
Yongqin Xian, Zeynep Akata, Gaurav Sharma +3
We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification. The proposed method…
Multi-Cue Zero-Shot Learning with Strong Supervision
Zeynep Akata, Mateusz Malinowski, Mario Fritz +1
Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training da…
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…