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Found 15 papers, 8 papers with code
BACS: Background Aware Continual Semantic Segmentation
Besides the common problem of classical catastrophic forgetting in the continual learning setting, CSS suffers from the inherent ambiguity of the background, a phenomenon we refer to as the "background shift'', since pixels labeled as background could correspond to future classes (forward background shift) or previous classes (backward background shift).
Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
However, image-to point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the contrastive losses pushing away semantically similar point and image regions and thus disturbing the local semantic structure of the learned representations, and 2) severe class imbalance as pretraining gets dominated by over-represented classes.
Estimating Regression Predictive Distributions with Sample Networks
A common approach to model uncertainty is to choose a parametric distribution and fit the data to it using maximum likelihood estimation.
Bayesian Embeddings for Few-Shot Open World Recognition
As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information.
Categorical Depth Distribution Network for Monocular 3D Object Detection
We validate our approach on the KITTI 3D object detection benchmark, where we rank 1st among published monocular methods.
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean.
Predictive Uncertainty in Deep Object Detectors: Estimation and Evaluation
We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean.
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
Next, we present a strict comparative study for probabilistic object detection based on an image detector and three public autonomous driving datasets.
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors
When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions.
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation
Training 3D object detectors for autonomous driving has been limited to small datasets due to the effort required to generate annotations.
Unlimited Road-scene Synthetic Annotation (URSA) Dataset
Utilizing open-source tools and resources found in single-player modding communities, we provide a method for persistent, ground truth, asset annotation of a game world.
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection
The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework.
In Defense of Classical Image Processing: Fast Depth Completion on the CPU
With the rise of data driven deep neural networks as a realization of universal function approximators, most research on computer vision problems has moved away from hand crafted classical image processing algorithms.
Joint 3D Proposal Generation and Object Detection from View Aggregation
We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.
Identifying Good Training Data for Self-Supervised Free Space Estimation
This paper proposes a novel technique to extract training data from free space in a scene using a stereo camera.
