10 citations · 10 across the 1 of their papers we have counts for
8 papers
Learning to Rank for Active Learning: A Listwise Approach
Minghan Li, Xialei Liu, Joost van de Weijer +1
Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous…
Hallucinating Saliency Maps for Fine-Grained Image Classification for Limited Data Domains
Carola Figueroa-Flores, Bogdan Raducanu, David Berga +1
Most of the saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline, like for instance, image classi…
Generative Feature Replay For Class-Incremental Learning
Xialei Liu, Chenshen Wu, Mikel Menta +5
Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We co…
Temporal Coherence for Active Learning in Videos
Javad Zolfaghari Bengar, Abel Gonzalez-Garcia, Gabriel Villalonga +5
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Th…
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation
Idoia Ruiz, Bogdan Raducanu, Rakesh Mehta +1
Finding a person across a camera network plays an important role in video surveillance. For a real-world person re-identification application, in order to guarantee an optimal time…
Memory Replay GANs: learning to generate images from new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu +3
Previous works on sequential learning address the problem of forgetting in discriminative models. In this paper we consider the case of generative models. In particular, we investi…