Uncertainty Estimation in Instance Segmentation of Affordances via Bayesian Visual Transformers

TL;DR

This paper introduces a Bayesian attention-based model for instance segmentation of visual affordances, improving accuracy and uncertainty estimation, with applications in robotics, AR, and prosthetics.

Contribution

It extends attention-based architectures with Bayesian ensembles for uncertainty quantification and proposes a novel measure for probabilistic mask quality.

Findings

Achieved +7.4 percentage points in $F_{eta}^w$ score on IIT-Aff dataset.

Bayesian models produce better-calibrated probabilities and less overconfidence.

Uncertainty estimates correlate with object contours and challenging pixels.

Abstract

Visual affordances identify regions in an image with potential interactions, offering a novel paradigm for scene understanding. Recognizing affordances allows autonomous robots to act more naturally, could enhance human-robot interactions, enrich augmented reality systems, and benefit prosthetic vision devices. Accurate and localized prediction of affordance regions, rather than general saliency maps is crucial for these applications. We present a model for instance segmentation of affordances by adopting sample-based and ensembles approaches for uncertainty estimation. We extend an attention-based architecture for our novel task, showing with detailed ablation experiments the effects of each component. By comparing the distribution of these different detections, we extract pixel-wise epistemic and aleatoric variances at both the semantic and spatial levels. In addition, we propose a novel measure called Probability-based Mask Quality, which enables a comprehensive analysis of semantic and spatial variations in a probabilistic instance segmentation model. Our results show that the global consensus of multiple sub-networks of Bayesian models improve deterministic networks due to a better mask refinement and generalization. This fact, joined with the more powerful features extracted by attention-based mechanisms, represent an improvement of +7.4 p.p on the $F_{\beta}^w$ score in the challenging IIT-Aff dataset. Bayesian models are also better calibrated, producing less overconfident probabilities and with a better uncertainty estimation. Qualitative results show that aleatoric variance appears in the contour of the objects, while the epistemic variance is observed in visual challenging pixels, adding interpretability to the neural network.