Most models for Weakly Supervised Video Anomaly Detection (WS-VAD) rely on multiple instance learning, aiming to distinguish normal and abnormal snippets without specifying the type of anomaly. However, the ambiguous nature of anomaly definitions across contexts may introduce inaccuracy in discriminating abnormal and normal events. To show the model what is anomalous, a novel framework is proposed to guide the learning of suspected anomalies from event prompts. Given a textual prompt dictionary of potential anomaly events and the captions generated from anomaly videos, the semantic anomaly similarity between them could be calculated to identify the suspected events for each video snippet. It enables a new multi-prompt learning process to constrain the visual-semantic features across all videos, as well as provides a new way to label pseudo anomalies for self-training. To demonstrate its effectiveness, comprehensive experiments and detailed ablation studies are conducted on four datasets, namely XD-Violence, UCF-Crime, TAD, and ShanghaiTech. Our proposed model outperforms most state-of-the-art methods in terms of AP or AUC (86.5%, 90.4%, 94.4%, and 97.4%). Furthermore, it shows promising performance in open-set and cross-dataset cases. The data, code, and models can be found at: https://github.com/shiwoaz/lap .

The vision transformer (ViT) has recently emerged as a leading approach in various domains, outperforming other methods. Therefore, it is logical to explore the possibility of transferring the superior knowledge from ViT to more compact and cost-effective convolutional neural networks (CNNs). However, due to substantial architectural disparities in representation and logits between these models, conventional knowledge distillation methods have proven ineffective in this context. To address this issue, a novel cross-architecture knowledge distillation scheme based on teacher collaboration is proposed to alleviate the architecture gap. Two different teachers, i.e. one ViT and one CNN, are utilized to simultaneously distill the student by feature reaggregation and logit correction. The experiments show that the proposed scheme outperforms conventional methods on CIFAR-100 dataset. The code is available at https://github.com/SunkiLin/RCD.

Knowledge distillation aims to achieve model compression by transferring knowledge from complex teacher models to lightweight student models. To reduce reliance on pre-trained teacher models, self-distillation methods utilize knowledge from the model itself as additional supervision. However, their performance is limited by the same or similar network architecture between the teacher and student. In order to increase architecture variety, we propose a new self-distillation framework called restructured self-distillation (RSD), which involves restructuring both the teacher and student networks. The self-distilled model is expanded into a multi-branch topology to create a more powerful teacher. During training, diverse student sub-networks are generated by randomly discarding the teacher's branches. Additionally, the teacher and student models are linked by a randomly inserted feature mixture block, introducing additional knowledge distillation in the mixed feature space. To avoid extra inference costs, the branches of the teacher model are then converted back to its original structure equivalently. Comprehensive experiments have demonstrated the effectiveness of our proposed framework for most architectures on CIFAR-10/100 and ImageNet datasets. Code is available at https://github.com/YujieZheng99/RSD.

Unsupervised video anomaly detection (UVAD) has gained significant attention due to its label-free nature. Typically, UVAD methods can be categorized into two branches, i.e. the one-class classification (OCC) methods and fully UVAD ones. However, the former may suffer from data imbalance and high false alarm rates, while the latter relies heavily on feature representation and pseudo-labels. In this paper, a novel feature reconstruction and disruption model (FRD-UVAD) is proposed for effective feature refinement and better pseudo-label generation in fully UVAD, based on cascade cross-attention transformers, a latent anomaly memory bank and an auxiliary scorer. The clip features are reconstructed using the space-time intra-clip information, as well as cross-inter-clip knowledge. Moreover, instead of blindly reconstructing all training features as OCC methods, a new disruption process is proposed to cooperate with the feature reconstruction simultaneously. Using the collected pseudo anomaly samples, it is able to emphasize the feature differences between normal and abnormal events. Additionally, a pre-trained UVAD scorer is utilized as a different criteria for anomaly prediction, which further refines the pseudo-labels. To demonstrate its effectiveness, comprehensive experiments and detailed ablation studies are conducted on three video benchmarks, namely CUHK Avenue, ShanghaiTech and UCF-Crime. Our proposed model (FRD-UVAD) achieves the best AUC performance (91.23%, 80.14%, and 82.12%) on all three datasets, surpassing other state-of-the-art OCC and fully UVAD methods. Furthermore, it obtains the lowest false alarm rate with a lower scene dependency, compared with other OCC methods. The code is available athttps://github.com/tcc-power/FRD-unsupervised-video-anomaly-detection.

Few-shot object detection (FSOD) enables the detector to recognize novel objects only using limited training samples, which could greatly alleviate model’s dependency on data. Most existing methods include two training stages, namely base training and fine-tuning. However, the unlabeled novel instances in the base set were untouched in previous works, which can be re-used to enhance the FSOD performance. Thus, a new instance mining model is proposed in this paper to excavate the novel samples from the base set. The detector is thus fine-tuned again by these additional free novel instances. Meanwhile, a novel pseudo-margin evaluation algorithm is designed to address the quality problem of pseudo-labels brought by those new novel instances. The experimental results on MS-COCO dataset show the effectiveness of the proposed model, which does not require any additional training samples or parameters. Our code is available at: https://github.com/liuweijie19980216/NimPme.