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Found 9 papers, 4 papers with code
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained.
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection
Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning.
Ranked #10 on
Semantic Segmentation
on SYN-UDTIRI
Trajectory Prediction for Autonomous Driving with Topometric Map
The experimental results show that the proposed method outperforms state-of-the-art multimodal methods and is robust to the perturbations of the topometric map.
Attentional Graph Neural Network for Parking-slot Detection
Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection.
Self-supervised Domain Adaptation for Computer Vision Tasks
Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration.
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example
However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA).
Hierarchical Adaptive Structural SVM for Domain Adaptation
In both cases, we show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data.
