Hybrid Open-set Segmentation with Synthetic Negative Data

Open-set segmentation is often conceived by complementing closed-set classification with anomaly detection. Existing dense anomaly detectors operate either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches optimize different objectives and therefore exhibit different failure modes. Consequently, we propose the first dense hybrid anomaly score that fuses generative and discriminative cues. The proposed score can be efficiently implemented by upgrading any semantic segmentation model with translation-equivariant estimates of data likelihood and dataset posterior. Our design is a remarkably good fit for efficient inference on large images due to negligible computational overhead over the closed-set baseline. The resulting dense hybrid open-set models require negative training images that can be sampled either from an auxiliary negative dataset or from a jointly trained generative model. We evaluate our contributions on benchmarks for dense anomaly detection and open-set segmentation of traffic scenes. The experiments reveal strong open-set performance in spite of negligible computational overhead

On Advantages of Mask-level Recognition for Outlier-aware Segmentation

Most dense recognition approaches bring a separate decision in each particular pixel. These approaches deliver competitive performance in usual closed-set setups. However, important applications in the wild typically require strong performance in presence of outliers. We show that this demanding setup greatly benefit from mask-level predictions, even in the case of non-finetuned baseline models. Moreover, we propose an alternative formulation of dense recognition uncertainty that effectively reduces false positive responses at semantic borders. The proposed formulation produces a further improvement over a very strong baseline and sets the new state of the art in outlier-aware semantic segmentation with and without training on negative data. Our contributions also lead to performance improvement in a recent panoptic setup. In-depth experiments confirm that our approach succeeds due to implicit aggregation of pixel-level cues into mask-level predictions.Comment: Accepted to CVPR 2023 workshop on Visual Anomaly and Novelty Detection (VAND

Dense out-of-distribution detection by using generative models

Semantička segmentacija slika važan je zadatak računalnog vida. Najbolji rezultati u tom području postižu se dubokim diskriminativnim konvolucijskim modelima koji su skloni neopravdanom optimizmu. U ovom radu adresiramo navedeni problem korištenjem primjera s ruba distribucije podataka nastalih uzorkovanjem generativnog modela temeljenog na invertibilnom normalizirajućem toku. Primjeri s ruba distribucije podataka u kombinaciji s primjerima iz negativnog skupa podataka drastično povećavaju performanse diskriminativnog modela u detekciji dijelova slike koji sadrže anomaliju. Navedene tvrdnje su vrednovane iscrpnim eksperimentima.Semantic segmentation is an important task in the field of computer vision. Current state of the art results are obtained by deep discriminative convolutional models which are known for its unjustified optimism. We address this issue by using samples at the distribution border obtained by sampling the flow-based generative model. Samples at the data distribution border in combination with samples from another negative dataset drastically improve discriminative model's performance in anomaly detection. All claims are evaluated on exhaustive experiments

Dense out-of-distribution detection by using generative models

Semantička segmentacija slika važan je zadatak računalnog vida. Najbolji rezultati u tom području postižu se dubokim diskriminativnim konvolucijskim modelima koji su skloni neopravdanom optimizmu. U ovom radu adresiramo navedeni problem korištenjem primjera s ruba distribucije podataka nastalih uzorkovanjem generativnog modela temeljenog na invertibilnom normalizirajućem toku. Primjeri s ruba distribucije podataka u kombinaciji s primjerima iz negativnog skupa podataka drastično povećavaju performanse diskriminativnog modela u detekciji dijelova slike koji sadrže anomaliju. Navedene tvrdnje su vrednovane iscrpnim eksperimentima.Semantic segmentation is an important task in the field of computer vision. Current state of the art results are obtained by deep discriminative convolutional models which are known for its unjustified optimism. We address this issue by using samples at the distribution border obtained by sampling the flow-based generative model. Samples at the data distribution border in combination with samples from another negative dataset drastically improve discriminative model's performance in anomaly detection. All claims are evaluated on exhaustive experiments

Evolution of Optimal Artificial Neural Network Architecture

U ovom radu su obrađeni postupci traženja optimalne arhitekture umjetne neuronske mreže. U radu je opisan i izrađen programski kod koji implementira indirektni pristup traženja optimalne arhitekture neuronske mreže uz pomoć genetskog algoritma. Također su objašnjeni optimizacijski algoritmi korišteni u samom postupku. Na kraju je postupak testiran na skupu podataka Iris te su prikazani rezultati.This work describes algorithms used for evolving artificial neural networks. Within this work, the direct coding scheme is used with a genetic algorithm. The work also describes optimization algorithms used in evolving of optimal artificial neural network architecture. In the end, the implemented algorithm is tested on well-known Iris dataset. The results are also described in this work

Dense out-of-distribution detection by using generative models

Semantička segmentacija slika važan je zadatak računalnog vida. Najbolji rezultati u tom području postižu se dubokim diskriminativnim konvolucijskim modelima koji su skloni neopravdanom optimizmu. U ovom radu adresiramo navedeni problem korištenjem primjera s ruba distribucije podataka nastalih uzorkovanjem generativnog modela temeljenog na invertibilnom normalizirajućem toku. Primjeri s ruba distribucije podataka u kombinaciji s primjerima iz negativnog skupa podataka drastično povećavaju performanse diskriminativnog modela u detekciji dijelova slike koji sadrže anomaliju. Navedene tvrdnje su vrednovane iscrpnim eksperimentima.Semantic segmentation is an important task in the field of computer vision. Current state of the art results are obtained by deep discriminative convolutional models which are known for its unjustified optimism. We address this issue by using samples at the distribution border obtained by sampling the flow-based generative model. Samples at the data distribution border in combination with samples from another negative dataset drastically improve discriminative model's performance in anomaly detection. All claims are evaluated on exhaustive experiments

Evolution of Optimal Artificial Neural Network Architecture

U ovom radu su obrađeni postupci traženja optimalne arhitekture umjetne neuronske mreže. U radu je opisan i izrađen programski kod koji implementira indirektni pristup traženja optimalne arhitekture neuronske mreže uz pomoć genetskog algoritma. Također su objašnjeni optimizacijski algoritmi korišteni u samom postupku. Na kraju je postupak testiran na skupu podataka Iris te su prikazani rezultati.This work describes algorithms used for evolving artificial neural networks. Within this work, the direct coding scheme is used with a genetic algorithm. The work also describes optimization algorithms used in evolving of optimal artificial neural network architecture. In the end, the implemented algorithm is tested on well-known Iris dataset. The results are also described in this work

Dense anomaly detection by robust learning on synthetic negative data

Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be partially anomalous. Previous work has addressed dense anomaly detection by discriminative training on mixed-content images. We extend this approach with synthetic negative patches which simultaneously achieve high inlier likelihood and uniform discriminative prediction. We generate synthetic negatives with normalizing flows due to their outstanding distribution coverage and capability to generate samples at different resolutions. We also propose to detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on standard benchmarks and datasets in spite of minimal computational overhead and refraining from auxiliary negative data