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Found 8 papers, 3 papers with code
Sanity Checks for Saliency Methods Explaining Object Detectors
Adebayo et al.'s work on evaluating saliency methods for classification models illustrate certain explanation methods fail the model and data randomization tests.
DExT: Detector Explanation Toolkit
State-of-the-art object detectors are treated as black boxes due to their highly non-linear internal computations.
Unsupervised Difficulty Estimation with Action Scores
Evaluating difficulty and biases in machine learning models has become of extreme importance as current models are now being applied in real-world situations.
Black-Box Optimization of Object Detector Scales
In this work, we propose the use of Black-box optimization methods to tune the prior/default box scales in Faster R-CNN and SSD, using Bayesian Optimization, SMAC, and CMA-ES.
Perception for Autonomous Systems (PAZ)
In this paper we introduce the Perception for Autonomous Systems (PAZ) software library.
Learning of Multi-Context Models for Autonomous Underwater Vehicles
Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system's dynamics.
Image Captioning and Classification of Dangerous Situations
Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks.
Real-time Convolutional Neural Networks for Emotion and Gender Classification
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs.
