150 papers with code • 1 benchmarks • 4 datasets
We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics.
In this paper, we propose a predict-refine architecture, BASNet, and a new hybrid loss for Boundary-Aware Salient object detection.
Ranked #1 on Salient Object Detection on DUTS-TE
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs.
While it is well known that autoregressive models come with a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and, together, exploit the probabilistic structure in the latents better than all previous learned models.