210 citations · 413 across the 8 of their papers we have counts for
6 papers · 1 filter
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey
Yanbei Chen, Massimiliano Mancini, Xiatian Zhu +1
State-of-the-art deep learning models are often trained with a large amount of costly labeled training data. However, requiring exhaustive manual annotations may degrade the model'…
Abstracting Sketches through Simple Primitives
Stephan Alaniz, Massimiliano Mancini, Anjan Dutta +2
Humans show high-level of abstraction capabilities in games that require quickly communicating object information. They decompose the message content into multiple parts and commun…
Temporal and cross-modal attention for audio-visual zero-shot learning
Otniel-Bogdan Mercea, Thomas Hummel, A. Sophia Koepke +1
Audio-visual generalised zero-shot learning for video classification requires understanding the relations between the audio and visual information in order to be able to recognise…
BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks
Uddeshya Upadhyay, Shyamgopal Karthik, Yanbei Chen +2
High-quality calibrated uncertainty estimates are crucial for numerous real-world applications, especially for deep learning-based deployed ML systems. While Bayesian deep learning…
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…
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…