Canadian Dairy Demand

The Canadian dairy industry faces a changing market environment as processors react to apparent shifts in consumers' preferences, consumers react to an altered mix of products on retail dairy shelves, and industry adjusts to potential pressures of competition and the challenge of new market opportunities under the impetus of changes arising from international trade. The purpose of this study is to derive a set of updated and disaggregated estimates of demand for major dairy products in a manner consistent with the economic theory of consumer behaviour. These estimates are necessary for policy models, policy analysis and forecasting. Previously dairy demand estimates were only available for broad product groupings such as fluid milk, butter, all cheese and "all other dairy products". For this study, four weakly separable groupings of major dairy products and related foods are specified. These are milk and other beverages, fats and oils, dairy dessert and related products and cheeses and apparent substitutes. Skim milk powder is assessed not to be a member of any of these groups but is hypothesized to be a member of a fifth dairy subgroup of dairy protein products. Due to data limitations, it was necessary to follow a single-equation approach for this product. The appropriateness of each product grouping was assessed by a two-stage test. First, each subgroup was tested using non-parametric tests of the axioms of revealed preference, as a means of inferring whether or not choices within each subgrouping are consistent with constrained utility maximization. Second, parametric assessment of each subgroup gave further evidence regarding the appropriateness of the groupings in terms of whether the estimated demand parameters are relatively stable and plausible. Based on satisfactory performance in these tests, parametric analyses for each subgroup were conducted using the linearized version of the almost ideal demand system, incorporating appropriate seasonality and habit formation variables. Estimates of own-price, cross-price and expenditure elasticities of demand are derived and presented. In general these seem plausible. Signs on the own-price elasticity estimates are as expected; the magnitudes appear to be reasonable. As expected, the majority of the specified foods are price-inelastic. However, butter, cooking/salad oil and other cheese appear to be price-elastic. Yogurt, concentrated milk and ice cream are fairly expenditure elastic while the two cheese types and butter appear slightly expenditure elastic.Demand and Price Analysis,

A Nutraceutical Industry: Policy Implications for Future Directions

Food Consumption/Nutrition/Food Safety,

Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies% at the frame level, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., ``vandalism'', is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks, UCFCrime-AR and XDViolence-AR, constructed on top of prevalent anomaly datasets. Meanwhile, we design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Canadian Consumers’ Purchasing Behavior of Omega-3 Products

The development of innovative functional food products is a major trend in today’s food industry. The growth of this industry is driven by increased consumer awareness of their own health deficiencies, increased understanding of the possible health benefits of functional foods, development in formulation technologies, a positive regulatory environment, and changing consumer demographics and lifestyles. While there has been a proliferation of omega-3 products such as milk, eggs, yogurt, and margarine in the Canadian food market, very little is known about consumers of these products. We use ACNielsen Homescanâ„¢ data combined with survey data to develop profiles of omega-3 consumers in Canada. The focus of the study is on consumers of four products: omega-3 milk, omega-3 yogurt, omega-3 margarine, and omega-3 eggs. We investigate whether there are significant differences between consumers and non-consumers of omega-3 products based on their age, income, education, and household composition. We also investigate whether a household’s use of Canada’s Food Guide and the Nutrition Facts table and consideration of the health benefits of food influences the decision to purchase omega-3 products. The results from the ordered probit model estimation show that the aging Canadian population is a major driver of omega-3 purchases. Also, the presence of children in the home increases the purchasing frequency of omega-3 yogurt and omega-3 margarine, and reading the Nutrition Facts table and considering the health benefits of food are important factors that affect omega-3 product purchases.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

Canadian Consumer Attitudes and Purchasing Behaviour of Omega-3 Products

The development of innovative functional food products is a major trend in today's food industry. The growth of this industry is driven by increased consumer awareness of their own health deficiencies, increased understanding of the possible health benefits of functional foods, development in formulation technologies, a positive regulatory environment and changing consumer demographics and lifestyles. While there has been a proliferation of omega-3 products such as milk, eggs, yogurt, and margarine in the Canadian food market, very little is known about consumers of omega-3 products. In our study we use ACNielsen HomescanTM data combined with ACNielsen Panel TrackTM survey data to develop profiles of omega-3 consumers in Canada. The focus of the study is on consumers of four products: omega-3 milk, omega-3 yogurt, omega-3 margarine and omega-3 eggs. We investigate whether there are significant differences between consumers and non-consumers of omega-3 products based on their age, income, education, and household composition. We also investigate whether a household's knowledge of the Canadian food guide, knowledge of nutrition labels, and consideration of health benefits influences the decision to purchase omega-3 products. The results from the ordered probit model estimation show that an aging (baby boomer) population is the most frequent purchaser of omega-3 products, the presence of children in the home increases the purchasing frequency of omega-3 yogurt and omega-3 margarine, and reading the Nutrition Facts panel and health benefits are important factors that affect the purchase of omega-3 products.omega-3 fatty acids, nutritional labelling, health benefits, ordered probit model, Food Consumption/Nutrition/Food Safety, C81, D12, I19, Q19,

A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation

Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task for preventing the occurrence of anomalous events. To this end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for video anomaly anticipation. We further propose a novel model capable of detecting and anticipating anomalous events simultaneously. Compared with 7 outstanding VAD algorithms in recent years, our method can cope with scene-dependent anomaly detection and anomaly anticipation both well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and the newly proposed NWPU Campus datasets consistently. Our dataset and code is available at: https://campusvad.github.io.Comment: CVPR 202

Open-Vocabulary Video Anomaly Detection

Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. However, current approaches are inherently limited to a closed-set setting and may struggle in open-world applications where there can be anomaly categories in the test data unseen during training. A few recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. However, such a setting focuses on predicting frame anomaly scores, having no ability to recognize the specific categories of anomalies, despite the fact that this ability is essential for building more informed video surveillance systems. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies. To this end, we propose a model that decouples OVVAD into two mutually complementary tasks -- class-agnostic detection and class-specific classification -- and jointly optimizes both tasks. Particularly, we devise a semantic knowledge injection module to introduce semantic knowledge from large language models for the detection task, and design a novel anomaly synthesis module to generate pseudo unseen anomaly videos with the help of large vision generation models for the classification task. These semantic knowledge and synthesis anomalies substantially extend our model's capability in detecting and categorizing a variety of seen and unseen anomalies. Extensive experiments on three widely-used benchmarks demonstrate our model achieves state-of-the-art performance on OVVAD task.Comment: Submitte

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