CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection

Zaheer, Muhammad Zaigham; Mahmood, Arif; Astrid, Marcella; Lee, Seung-Ik

doi:10.1007/978-3-030-58542-6_22

Cited by 93 publications

(30 citation statements)

References 49 publications

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“…Video Anomaly Detection This paper focuses on framelevel VAD. There are also recent works that focus on videolevel week supervision [16], [17], [18] and object-level detection [19], [20], which beyond our discussion in this paper.…”

Section: Related Workmentioning

confidence: 98%

Discriminative-Generative Dual Memory Video Anomaly Detection

Guo,

Jin,

Chen

et al. 2021

Preprint

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Recently, people tried to use a few anomalies for video anomaly detection (VAD) instead of only normal data during the training process. A side effect of data imbalance occurs when a few abnormal data face a vast number of normal data. The latest VAD works use triplet loss or data re-sampling strategy to lessen this problem. However, there is still no elaborately designed structure for discriminative VAD with a few anomalies. In this paper, we propose a DiscRiminative-gEnerative duAl Memory (DREAM) anomaly detection model to take advantage of a few anomalies and solve data imbalance. We use two shallow discriminators to tighten the normal feature distribution boundary along with a generator for the next frame prediction. Further, we propose a dual memory module to obtain a sparse feature representation in both normality and abnormality space. As a result, DREAM not only solves the data imbalance problem but also learn a reasonable feature space. Further theoretical analysis shows that our DREAM also works for the unknown anomalies. Comparing with the previous methods on UCSD Ped1, UCSD Ped2, CUHK Avenue, and ShanghaiTech, our model outperforms all the baselines with no extra parameters. The ablation study demonstrates the effectiveness of our dual memory module and discriminative-generative network.

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Section: Related Workmentioning

confidence: 98%

Discriminative-Generative Dual Memory Video Anomaly Detection

Guo,

Jin,

Chen

et al. 2021

Preprint

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“…Baselines. We train six SOTA WVAD baselines: DeepMIL [19], GCN-Ano [30], CLAWS [29], AR-Net [26], MIST [7], and RTFM The same experimental setup as our approach is applied to these baselines for fair comparison. Evaluation Measures.…”

Section: Evaluation On Polyp Frame Detectionmentioning

confidence: 99%

Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Tian¹,

Pang²,

Liu³

et al. 2022

Preprint

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Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps. Consequently, they often have high detection errors, especially on challenging polyp cases (e.g., small, flat, or partially visible polyps). In this work, we formulate polyp detection as a weakly-supervised anomaly detection task that uses video-level labelled training data to detect frame-level polyps. In particular, we propose a novel convolutional transformer-based multiple instance learning method designed to identify abnormal frames (i.e., frames with polyps) from anomalous videos (i.e., videos containing at least one frame with polyp). In our method, local and global temporal dependencies are seamlessly captured while we simultaneously optimise video and snippet-level anomaly scores. A contrastive snippet mining method is also proposed to enable an effective modelling of the challenging polyp cases. The resulting method achieves a detection accuracy that is substantially better than current state-of-the-art approaches on a new large-scale colonoscopy video dataset introduced in this work. Our code and dataset will be publicly available upon acceptance.

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“…Our method may also be seen as a variant of negative learning [43] in which few anomaly examples along with the inlier data are provided to the system during training to enhance the decision boundary. We also acknowledge a recent introduction of several weakly supervised methods which use both normal and anomalous examples for training [30], [44]- [47]. Mostly popular for videos, these methods utilize video-level weak supervision to train the frame-level prediction models.…”

Section: Related Workmentioning

confidence: 99%

Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies

Zaheer¹,

Lee²,

Mahmood³

et al. 2022

Preprint

Self Cite

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Recently, anomaly scores have been formulated using reconstruction loss of the adversarially learned generators and/or classification loss of discriminators. Unavailability of anomaly examples in the training data makes optimization of such networks challenging. Attributed to the adversarial training, performance of such models fluctuates drastically with each training step, making it difficult to halt the training at an optimal point. In the current study, we propose a robust anomaly detection framework that overcomes such instability by transforming the fundamental role of the discriminator from identifying real vs. fake data to distinguishing good vs. bad quality reconstructions. For this purpose, we propose a method that utilizes the current state as well as an old state of the same generator to create good and bad quality reconstruction examples. The discriminator is trained on these examples to detect the subtle distortions that are often present in the reconstructions of anomalous data. In addition, we propose an efficient generic criterion to stop the training of our model, ensuring elevated performance. Extensive experiments performed on six datasets across multiple domains including image and video based anomaly detection, medical diagnosis, and network security, have demonstrated excellent performance of our approach.

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CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection

Cited by 93 publications

References 49 publications

Discriminative-Generative Dual Memory Video Anomaly Detection

Discriminative-Generative Dual Memory Video Anomaly Detection

Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomalies

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