16 research outputs found
Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
Real-world deployment of reliable object detectors is crucial for
applications such as autonomous driving. However, general-purpose object
detectors like Faster R-CNN are prone to providing overconfident predictions
for outlier objects. Recent outlier-aware object detection approaches estimate
the density of instance-wide features with class-conditional Gaussians and
train on synthesized outlier features from their low-likelihood regions.
However, this strategy does not guarantee that the synthesized outlier features
will have a low likelihood according to the other class-conditional Gaussians.
We propose a novel outlier-aware object detection framework that learns to
distinguish outliers from inlier objects by learning the joint data
distribution of all inlier classes with an invertible normalizing flow. The
flow model ensures that the synthesized outliers have a lower likelihood than
inliers of all object classes, thereby modeling a better decision boundary
between inlier and outlier objects. Our approach significantly outperforms the
state-of-the-art for outlier-aware object detection on both image and video
datasets.Comment: 15 page
Quantile-based Maximum Likelihood Training for Outlier Detection
Discriminative learning effectively predicts true object class for image
classification. However, it often results in false positives for outliers,
posing critical concerns in applications like autonomous driving and video
surveillance systems. Previous attempts to address this challenge involved
training image classifiers through contrastive learning using actual outlier
data or synthesizing outliers for self-supervised learning. Furthermore,
unsupervised generative modeling of inliers in pixel space has shown limited
success for outlier detection. In this work, we introduce a quantile-based
maximum likelihood objective for learning the inlier distribution to improve
the outlier separation during inference. Our approach fits a normalizing flow
to pre-trained discriminative features and detects the outliers according to
the evaluated log-likelihood. The experimental evaluation demonstrates the
effectiveness of our method as it surpasses the performance of the
state-of-the-art unsupervised methods for outlier detection. The results are
also competitive compared with a recent self-supervised approach for outlier
detection. Our work allows to reduce dependency on well-sampled negative
training data, which is especially important for domains like medical
diagnostics or remote sensing.Comment: Code available at https://github.com/taghikhah/QuantO
Enhancing Fairness of Visual Attribute Predictors
The performance of deep neural networks for image recognition tasks such as
predicting a smiling face is known to degrade with under-represented classes of
sensitive attributes. We address this problem by introducing fairness-aware
regularization losses based on batch estimates of Demographic Parity, Equalized
Odds, and a novel Intersection-over-Union measure. The experiments performed on
facial and medical images from CelebA, UTKFace, and the SIIM-ISIC melanoma
classification challenge show the effectiveness of our proposed fairness losses
for bias mitigation as they improve model fairness while maintaining high
classification performance. To the best of our knowledge, our work is the first
attempt to incorporate these types of losses in an end-to-end training scheme
for mitigating biases of visual attribute predictors. Our code is available at
https://github.com/nish03/FVAP.Comment: Camera Ready, ACCV 202
The CAMP Lab Computer Aided Medical Procedures and Augmented Reality
Abstract-The CAMP lab is integrated within the Department of Informatics at Technical University of Munich and is considered one of the leading groups concerned with medical augmented reality, computer assisted interventions, as well as non-medical related computer vision. In this short paper, we give an outline of the history of the lab and present a summary of some of our past and current activities relevant to augmented and virtual reality in computer assisted interventions and surgeries. References to published work in major journals and conferences allow the reader to get access to more detailed information on each subject. It was not possible to cover all aspects of our research within this paper, but we hope to provide an overview on some of these within this short paper. The readers are also invited to visit our web-site at http://campar.in.tum.de to get more information on aspects of our work. Applications for PhD and PostDoc positions can be made through the form a