Search Results for author:
Found 11 papers, 7 papers with code
Weakly-Supervised Semantic Segmentation by Learning Label Uncertainty
In this paper, we present a new loss function to train a segmentation network with only a small subset of pixel-perfect labels, but take the advantage of weakly-annotated training samples in the form of cheap bounding-box labels.
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
In this work we propose a new clustering loss function for proposal-free instance segmentation.
Ranked #1000000000 on
Instance Segmentation
on Cityscapes test
A Three-Player GAN: Generating Hard Samples To Improve Classification Networks
The generator's objective is to synthesize samples that are both realistic and hard to label for the classifier.
Sparse and noisy LiDAR completion with RGB guidance anduncertainty
For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions.
Sparse and noisy LiDAR completion with RGB guidance and uncertainty
However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input.
Ranked #5 on
Depth Completion
on KITTI Depth Completion
End-to-end Lane Detection through Differentiable Least-Squares Fitting
The problem with such a two-step approach is that the parameters of the network are not optimized for the true task of interest (estimating the lane curvature parameters) but for a proxy task (segmenting the lane markings), resulting in sub-optimal performance.
Classification-Driven Dynamic Image Enhancement
In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.
Towards End-to-End Lane Detection: an Instance Segmentation Approach
By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation.
Ranked #15 on
Lane Detection
on TuSimple
Classification Driven Dynamic Image Enhancement
In this paper, we are interested in learning CNNs that can emulate image enhancement and restoration, but with the overall goal to improve image classification and not necessarily human perception.
Semantic Instance Segmentation with a Discriminative Loss Function
In this work we propose to tackle the problem with a discriminative loss function, operating at the pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.
Ranked #4 on
Multi-Human Parsing
on MHP v1.0
Fast Scene Understanding for Autonomous Driving
Most approaches for instance-aware semantic labeling traditionally focus on accuracy.
