60 citations · 70 across the 9 of their papers we have counts for
10 papers
Differentiable Inverse Graphics for Zero-shot Scene Reconstruction and Robot Grasping
Octavio Arriaga, Proneet Sharma, Jichen Guo +3
Operating effectively in novel real-world environments requires robotic systems to estimate and interact with previously unseen objects. Current state-of-the-art models address thi…
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning
Octavio Arriaga, Rebecca Adam, Melvin Laux +4
Real-world robotic applications, from autonomous exploration to assistive technologies, require adaptive, interpretable, and data-efficient learning paradigms. While deep learning…
Bayesian Inverse Graphics for Few-Shot Concept Learning
Octavio Arriaga, Jichen Guo, Rebecca Adam +2
Humans excel at building generalizations of new concepts from just one single example. Contrary to this, current computer vision models typically require large amount of training s…
Sanity Checks for Saliency Methods Explaining Object Detectors
Deepan Chakravarthi Padmanabhan, Paul G. Plöger, Octavio Arriaga +1
Saliency methods are frequently used to explain Deep Neural Network-based models. Adebayo et al.'s work on evaluating saliency methods for classification models illustrate certain…
Unsupervised Difficulty Estimation with Action Scores
Octavio Arriaga, Matias Valdenegro-Toro
Evaluating difficulty and biases in machine learning models has become of extreme importance as current models are now being applied in real-world situations. In this paper we pres…
Black-Box Optimization of Object Detector Scales
Mohandass Muthuraja, Octavio Arriaga, Paul Plöger +2
Object detectors have improved considerably in the last years by using advanced CNN architectures. However, many detector hyper-parameters are generally manually tuned, or they are…
Perception for Autonomous Systems (PAZ)
Octavio Arriaga, Matias Valdenegro-Toro, Mohandass Muthuraja +2
In this paper we introduce the Perception for Autonomous Systems (PAZ) software library. PAZ is a hierarchical perception library that allow users to manipulate multiple levels of…
Learning of Multi-Context Models for Autonomous Underwater Vehicles
Bilal Wehbe, Octavio Arriaga, Mario Michael Krell +1
Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system's dynamics. This work addresses the problem of identifying mu…
Image Captioning and Classification of Dangerous Situations
Octavio Arriaga, Paul Plöger, Matias Valdenegro-Toro
Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able…
Real-time Convolutional Neural Networks for Emotion and Gender Classification
Octavio Arriaga, Matias Valdenegro-Toro, Paul Plöger
In this paper we propose an implement a general convolutional neural network (CNN) building framework for designing real-time CNNs. We validate our models by creating a real-time v…