Search Results for author:
Found 14 papers, 7 papers with code
Modeling the Effects of Windshield Refraction for Camera Calibration
Instead of using a generalized camera approach, we propose a novel approach to jointly optimize a traditional camera model, and a mathematical representation of the windshield’s surface.
Contrastive Learning for Multi-Object Tracking with Transformers
The DEtection TRansformer (DETR) opened new possibilities for object detection by modeling it as a translation task: converting image features into object-level representations.
Ranked #1 on
Multiple Object Tracking
on BDD100K test
Unbalanced Optimal Transport: A Unified Framework for Object Detection
The approach is well suited for GPU implementation, which proves to be an advantage for large-scale models.
TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM
We start from an off-the-shelf 3D object detector, and apply a tracking mechanism where objects are matched by an affinity score computed on local object feature embeddings and motion descriptors.
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.
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation
We surpass camera independent methods on the challenging KITTI3D benchmark and show the key benefits compared to camera dependent methods.
Context-aware Padding for Semantic Segmentation
Zero padding is widely used in convolutional neural networks to prevent the size of feature maps diminishing too fast.
SCAN: Learning to Classify Images without Labels
First, a self-supervised task from representation learning is employed to obtain semantically meaningful features.
Ranked #4 on
Unsupervised Image Classification
on ImageNet
Multi-Task Learning for Dense Prediction Tasks: A Survey
In this survey, we provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision, explicitly emphasizing on dense prediction tasks.
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
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.
A Gaussian Process Latent Variable Model for BRDF Inference
Although inferring higher dimensional BRDFs from such modest training is not a trivial problem, our method performs better than state-of-the-art parametric, semi-parametric and non-parametric approaches.
