562 citations · 1.6k across the 39 of their papers we have counts for
8 papers · 1 filter
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Wei-Hong Li, Xialei Liu, Hakan Bilen
Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient appro…
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation
Arushi Goel, Basura Fernando, Frank Keller +1
Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects, which is essential for full scene understanding. Existing SGG methods trained…
Dataset Condensation with Distribution Matching
Bo Zhao, Hakan Bilen
Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets. A recent promising…
Cross-domain Few-shot Learning with Task-specific Adapters
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples. R…
Semi-supervised Viewpoint Estimation with Geometry-aware Conditional Generation
Octave Mariotti, Hakan Bilen
There is a growing interest in developing computer vision methods that can learn from limited supervision. In this paper, we consider the problem of learning to predict camera view…
Learning Foreground-Background Segmentation from Improved Layered GANs
Yu Yang, Hakan Bilen, Qiran Zou +2
Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task. In…
Universal Representation Learning from Multiple Domains for Few-shot Classification
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods…
Dataset Condensation with Differentiable Siamese Augmentation
Bo Zhao, Hakan Bilen
In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this pa…