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Found 10 papers, 1 papers with code
Object-Centric Open-Vocabulary Image-Retrieval with Aggregated Features
Our approach aggregates dense embeddings extracted from CLIP into a compact representation, essentially combining the scalability of image retrieval pipelines with the object identification capabilities of dense detection methods.
Polarimetric Imaging for Perception
Autonomous driving and advanced driver-assistance systems rely on a set of sensors and algorithms to perform the appropriate actions and provide alerts as a function of the driving scene.
3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation
3D-LaneNet+ is a camera-based DNN method for anchor free 3D lane detection which is able to detect 3d lanes of any arbitrary topology such as splits, merges, as well as short and perpendicular lanes.
Synthetic-to-Real Domain Adaptation for Lane Detection
In addition, our autoencoder approach outperforms all other methods in the semi-supervised domain adaptation scenario.
Semi-Local 3D Lane Detection and Uncertainty Estimation
We propose a novel camera-based DNN method for 3D lane detection with uncertainty estimation.
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Predicting not only the target but also an accurate measure of uncertainty is important for many machine learning applications and in particular safety-critical ones.
3D-LaneNet: End-to-End 3D Multiple Lane Detection
We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image.
Ranked #9 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Learning Discrete Weights Using the Local Reparameterization Trick
We show how a simple modification to the local reparameterization trick, previously used to train Gaussian distributed weights, enables the training of discrete weights.
Structured Hough Voting for Vision-based Highway Border Detection
We propose a vision-based highway border detection algorithm using structured Hough voting.
Fast Multiple-Part Based Object Detection Using KD-Ferns
Our algorithm is an accelerated version of the "Feature Synthesis" (FS) method [1], which uses multiple object parts for detection and is among state-of-theart methods on human detection benchmarks, but also suffers from a high computational cost.
