VadCLIP: Adapting Vision-Language Models for Weakly Supervised Video Anomaly Detection

The recent contrastive language-image pre-training (CLIP) model has shown great success in a wide range of image-level tasks, revealing remarkable ability for learning powerful visual representations with rich semantics. An open and worthwhile problem is efficiently adapting such a strong model to the video domain and designing a robust video anomaly detector. In this work, we propose VadCLIP, a new paradigm for weakly supervised video anomaly detection (WSVAD) by leveraging the frozen CLIP model directly without any pre-training and fine-tuning process. Unlike current works that directly feed extracted features into the weakly supervised classifier for frame-level binary classification, VadCLIP makes full use of fine-grained associations between vision and language on the strength of CLIP and involves dual branch. One branch simply utilizes visual features for coarse-grained binary classification, while the other fully leverages the fine-grained language-image alignment. With the benefit of dual branch, VadCLIP achieves both coarse-grained and fine-grained video anomaly detection by transferring pre-trained knowledge from CLIP to WSVAD task. We conduct extensive experiments on two commonly-used benchmarks, demonstrating that VadCLIP achieves the best performance on both coarse-grained and fine-grained WSVAD, surpassing the state-of-the-art methods by a large margin. Specifically, VadCLIP achieves 84.51% AP and 88.02% AUC on XD-Violence and UCF-Crime, respectively. Code and features will be released to facilitate future VAD research.Comment: Submitte

Take a Prior from Other Tasks for Severe Blur Removal

Recovering clear structures from severely blurry inputs is a challenging problem due to the large movements between the camera and the scene. Although some works apply segmentation maps on human face images for deblurring, they cannot handle natural scenes because objects and degradation are more complex, and inaccurate segmentation maps lead to a loss of details. For general scene deblurring, the feature space of the blurry image and corresponding sharp image under the high-level vision task is closer, which inspires us to rely on other tasks (e.g. classification) to learn a comprehensive prior in severe blur removal cases. We propose a cross-level feature learning strategy based on knowledge distillation to learn the priors, which include global contexts and sharp local structures for recovering potential details. In addition, we propose a semantic prior embedding layer with multi-level aggregation and semantic attention transformation to integrate the priors effectively. We introduce the proposed priors to various models, including the UNet and other mainstream deblurring baselines, leading to better performance on severe blur removal. Extensive experiments on natural image deblurring benchmarks and real-world images, such as GoPro and RealBlur datasets, demonstrate our method's effectiveness and generalization ability

Stock Market Prediction via Deep Learning Techniques: A Survey

The stock market prediction has been a traditional yet complex problem researched within diverse research areas and application domains due to its non-linear, highly volatile and complex nature. Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. Deep learning has dominated many domains, gained much success and popularity in recent years in stock market prediction. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction focusing on deep learning techniques. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks from 2011 to 2022. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we highlight some open issues and point out several future directions by sharing some new perspectives on stock market prediction

Kernel sparse tracking with compressive sensing

Online tracking is a challenging task to develop effective and efficient models to account for appearance change. However, most tracking algorithms only consider the holistic or local information and do not make full use of the appearance information. In this study, a novel tracking algorithm with sparse representation is proposed and the online classifier is learned to discriminate the target from the background. To reduce visual drift problem which is encountered in object tracking, a two‐stage sparse representation method is proposed. The holistic information is used to estimate the initial tracking position, and the local information is used to determine the final tracking position. To improve the performance of the classifier and robustness of the algorithm, the kernel function is applied on the sparse representation. Moreover, the dimension of the target is reduced via compressive sensing. Besides, a simple and effective method for dictionary update is proposed. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed algorithm performs favourably against several state‐of‐the‐art algorithms

The Regulatory Role of Casein Glycomacropeptide (CGMP) in the Fecal Flora of Mice with Ulcerative Colitis

ABSTRACT Objective: To explore the regulatory role of Casein glycomacropeptide (CGMP) in the fecal flora of mice with Ulcerative colitis (UC). Methods: The BALB/c mice were divided into a normal control group, a model group, CGMP groups with low doses (5 mg/kg·d), middle doses (50 mg/kg·d) and high doses (500 mg/kg·d), and a Sulfasalazine (SASP) group. Except the normal control group, all mice were treated with Oxazolone (OXZ) by perfusion to induce the UC model. In contrast, the mice in the normal control group and the model group were treated with an equal volume of saline for 7 consecutive days, which the changes in the fecal flora of the mouse intestines were evaluated by Fluorescence in situ hybridization (FISH) and DNA staining. Conclusion: CGMP can rectify the structural imbalances in the colon fecal flora in mice with UC and can improve the proportion of dominant flora in the intestine. CGMP at a dose of 50 mg/kg·d can significantly reduce the number of Enterobacteriaceae and Enterococci and promote the proliferation of Bifidobacteria, Lactobacilli, Bacteroides and Clostridium coccoides in the intestine

Modulation of mice fecal microbiota by administration of casein glycomacropeptide

Casein glycomacropeptide (GMP) is known to promote the in vitro growth of Bifidobacteria and Lactobacilli. In this paper, we used conven- tional culture techniques and fluorescent in situ hybridization (FISH) techniques to investigate the effect of casein GMP on mice fecal microbiota. The population structure of the intestinal microbiota, including Lactobacillus, Bifidobacteria, Enterococcus, coliforms and Enterobacteriaceae, was tested and compared. After consecutive administration of casein GMP for 15 days, numbers of Lactobacillus and Bifidobacteria increased significantly (P<0.01), numbers of Enterobacteriaceae and Coliforms decreased significantly (P<0.05) while no significant changes were observed for Enterococcus. The detection limits of FISH technique were significantly lower (P<0.01) than the traditional culture method. These results suggested that consumption of casein GMP had a prebiotic effect on male BALB/c mice. Casein GMP helped establish a healthier intestinal microbiota. Additionally, FISH was proved to be a rapid and relatively low-cost detection method that can be used to further our understanding of human intestinal microbiota