Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Yi, Jihun"'
Autor:
Yi, Jihun, Yoon, Sungroh
In this paper, we address the problem of unsupervised video anomaly detection (UVAD). The task aims to detect abnormal events in test video using unlabeled videos as training data. The presence of anomalies in the training data poses a significant ch
Externí odkaz:
http://arxiv.org/abs/2408.03014
The task of image anomaly detection (IAD) aims to identify deviations from normality in image data. These anomalies are patterns that deviate significantly from what the IAD model has learned from the data during training. However, in real-world scen
Externí odkaz:
http://arxiv.org/abs/2407.19849
Continuous efforts are being made to advance anomaly detection in various manufacturing processes to increase the productivity and safety of industrial sites. Deep learning replaced rule-based methods and recently emerged as a promising method for an
Externí odkaz:
http://arxiv.org/abs/2406.12260
In this paper, we primarily address the issue of dialogue-form context query within the interactive text-to-image retrieval task. Our methodology, PlugIR, actively utilizes the general instruction-following capability of LLMs in two ways. First, by r
Externí odkaz:
http://arxiv.org/abs/2406.03411
Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level info
Externí odkaz:
http://arxiv.org/abs/2103.08907
Several data augmentation methods deploy unlabeled-in-distribution (UID) data to bridge the gap between the training and inference of neural networks. However, these methods have clear limitations in terms of availability of UID data and dependence o
Externí odkaz:
http://arxiv.org/abs/2101.06639
To demystify the "black box" property of deep neural networks for natural language processing (NLP), several methods have been proposed to interpret their predictions by measuring the change in prediction probability after erasing each token of an in
Externí odkaz:
http://arxiv.org/abs/2010.13984
In this work, we attempt to explain the prediction of any black-box classifier from an information-theoretic perspective. For each input feature, we compare the classifier outputs with and without that feature using two information-theoretic metrics.
Externí odkaz:
http://arxiv.org/abs/2009.11150
We propose an interpretable Capsule Network, iCaps, for image classification. A capsule is a group of neurons nested inside each layer, and the one in the last layer is called a class capsule, which is a vector whose norm indicates a predicted probab
Externí odkaz:
http://arxiv.org/abs/2008.08756
Autor:
Yi, Jihun, Yoon, Sungroh
In this paper, we address the problem of image anomaly detection and segmentation. Anomaly detection involves making a binary decision as to whether an input image contains an anomaly, and anomaly segmentation aims to locate the anomaly on the pixel
Externí odkaz:
http://arxiv.org/abs/2006.16067