Search Results for author:
Found 9 papers, 4 papers with code
CSG0: Continual Urban Scene Generation with Zero Forgetting
With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving.
Semantic Palette: Guiding Scene Generation with Class Proportions
Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem.
QUEST: Quantized embedding space for transferring knowledge
Knowledge distillation refers to the process of training a compact student network to achieve better accuracy by learning from a high capacity teacher network.
This dataset does not exist: training models from generated images
This naturally brings the question: Can we train a classifier only on the generated data?
DADA: Depth-aware Domain Adaptation in Semantic Segmentation
As a result, the performance of the trained semantic segmentation model on the target domain is boosted.
Ranked #17 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Semantic segmentation is a key problem for many computer vision tasks.
Ranked #4 on
Domain Adaptation
on Panoptic SYNTHIA-to-Mapillary
Learning a Complete Image Indexing Pipeline
To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists.
SUBIC: A supervised, structured binary code for image search
For large-scale visual search, highly compressed yet meaningful representations of images are essential.
Approximate search with quantized sparse representations
This paper tackles the task of storing a large collection of vectors, such as visual descriptors, and of searching in it.
