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Found 14 papers, 9 papers with code
A Simple Latent Diffusion Approach for Panoptic Segmentation and Mask Inpainting
Panoptic and instance segmentation networks are often trained with specialized object detection modules, complex loss functions, and ad-hoc post-processing steps to handle the permutation-invariance of the instance masks.
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
Branched Multi-Task Networks: Deciding What Layers To Share
In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand.
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 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
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.
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.
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
Fast Scene Understanding for Autonomous Driving
Most approaches for instance-aware semantic labeling traditionally focus on accuracy.
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
Dynamic Steerable Blocks in Deep Residual Networks
Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account.
Dynamic Filter Networks
In a traditional convolutional layer, the learned filters stay fixed after training.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Energy-Efficient ConvNets Through Approximate Computing
Recently ConvNets or convolutional neural networks (CNN) have come up as state-of-the-art classification and detection algorithms, achieving near-human performance in visual detection.
