Search Results for author:
Found 6 papers, 0 papers with code
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving
3D object detection has recently received much attention due to its great potential in autonomous vehicle (AV).
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime
Recent studies combined finetuning (FT) from pretrained weights with SSL to mitigate the challenges and claimed superior results in the low-label regime.
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs
Our results strongly argue for the use of superpixel-based AL for semantic segmentation and highlight the importance of using realistic annotation costs in evaluating such methods.
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets.
MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors
Modern convolutional object detectors have improved the detection accuracy significantly, which in turn inspired the development of dedicated hardware accelerators to achieve real-time performance by exploiting inherent parallelism in the algorithm.
TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks
To this end, we introduce TEA-DNN, a NAS algorithm targeting multi-objective optimization of execution time, energy consumption, and classification accuracy of CNN workloads on embedded architectures.
