339 citations · 343 across the 3 of their papers we have counts for
6 papers
Active Learning for Deep Object Detection via Probabilistic Modeling
Jiwoong Choi, Ismail Elezi, Hyuk-Jae Lee +2
Active learning aims to reduce labeling costs by selecting only the most informative samples on a dataset. Few existing works have addressed active learning for object detection. M…
Scalable Active Learning for Object Detection
Elmar Haussmann, Michele Fenzi, Kashyap Chitta +7
Deep Neural Networks trained in a fully supervised fashion are the dominant technology in perception-based autonomous driving systems. While collecting large amounts of unlabeled d…
Training Data Subset Search with Ensemble Active Learning
Kashyap Chitta, Jose M. Alvarez, Elmar Haussmann +1
Deep Neural Networks (DNNs) often rely on very large datasets for training. Given the large size of such datasets, it is conceivable that they contain certain samples that either d…
Causal graph-based video segmentation
Camille Couprie, Clément Farabet, Yann LeCun
Numerous approaches in image processing and computer vision are making use of super-pixels as a pre-processing step. Among the different methods producing such over-segmentation of…
Indoor Semantic Segmentation using depth information
Camille Couprie, Clément Farabet, Laurent Najman +1
This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-craft…
Clustering Learning for Robotic Vision
Eugenio Culurciello, Jordan Bates, Aysegul Dundar +2
We present the clustering learning technique applied to multi-layer feedforward deep neural networks. We show that this unsupervised learning technique can compute network filters…