Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning

Learning discriminative features between abnormal and normal instances is crucial for video anomaly detection within the multiple instance learning framework. Existing methods primarily focus on instances with the highest anomaly scores, neglecting the identification and differentiation of hard samples, leading to misjudgments and high false alarm rates. To address these challenges, we propose a dual triplet contrastive loss strategy. This approach employs dual memory units to extract four key feature categories: hard samples, negative samples, positive samples, and anchor samples. Contrastive loss is utilized to constrain the distance between hard samples and other samples, enabling accurate identification of hard samples and enhancing the discriminative ability of hard samples and abnormal features. Additionally, a multi-scale feature perception module is designed to capture feature information at different levels, while an adaptive global-local feature fusion module constructs complementary feature enhancement through feature fusion. Experimental results demonstrate the effectiveness of our method, achieving AUC scores of 87.16% on the UCF-Crime dataset and AP scores of 83.47% on the XD-Violence dataset.

The research on action understanding has achieved significant progress with the establishment of various benchmark datasets. However, the results of action understanding are far from satisfactory in practice. One reason is that the existing action datasets ignore the existence of many hard negative samples in real-world scenarios, which are usually undefined confusion actions, e.g., holding a pen near the mouth vs. smoking. In this work, we focus on the common actions in our daily life and present a novel Common Daily Action Dataset (CDAD), which consists of 57,824 video clips of 23 well-defined common daily actions with rich manual annotations. Particularly, for each daily action, we collect not only diverse positive samples but also various hard negative samples that have minor differences (share similarities) in action with the positive ones. The established CDAD dataset could not only serve as a benchmark for several important daily action understanding tasks, including multi-label action recognition, temporal action localization, and spatial-temporal action detection, but also provide a testbed for researchers to investigate the influence of highly similar negative samples in learning action understanding models. Datasets and codes are available: https://github.com/MartinXM/CDAD.

Object tracking is an essential and challenging sub-domain in the field of computer vision owing to its wide range of applications and complexities of real-life situations. It has been studied extensively over the last decade, leading to the proposal of several tracking frameworks and approaches. Recently, the introduction of reinforcement learning and the Actor-Critic framework has effectively improved the tracking speed of deep learning trackers. However, most existing deep reinforcement learning trackers experience a slight performance degradation mainly owing to the drift issues. Drifts pose a threat to the tracking performance, which may lead to losing the tracked target. Herein, we propose a drift-proof tracker with deep reinforcement learning that aims to improve the tracking performance by counteracting drifts while maintaining its real-time advantage. We utilize a reward function with the Distance-IoU (DIoU) metric to guide the reinforcement learning to alleviate the drifts caused by the trained model. Furthermore, double negative samples (hard negative and drift samples) are constructed in tracking for network initialization, which is followed by calculating the loss by a small error-friendly loss function. Therefore, our tracker can better discriminate between the positive and negative samples and correct the predicted bounding boxes when the drift occurs. Meanwhile, a generative adversarial network is adopted for positive sample augmentation. Extensive experimental results on multiple popular benchmarks show that our algorithm effectively reduces the occurrences of drift and boosts the tracking performance, compared to those of other state-of-the-art trackers.

Learning an effective distance measurement between sample pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples. However, hard negative samples usually account for the tiny minority in the training set, which may fail to fully describe the data distribution close to the decision boundary. In this paper, we present a deep adversarial metric learning (DAML) framework to generate synthetic hard negatives from the original negative samples, which is widely applicable to existing supervised deep metric learning algorithms. Different from existing sampling strategies which simply ignore numerous easy negatives, our DAML aim to exploit them by generating synthetic hard negatives adversarial to the learned metric as complements. We simultaneously train the feature embedding and hard negative generator in an adversarial manner, so that adequate and targeted synthetic hard negatives are created to learn more precise distance metrics. As a single transformation may not be powerful enough to describe the global input space under the attack of the hard negative generator, we further propose a deep adversarial multi-metric learning (DAMML) method by learning multiple local transformations for more complete description. We simultaneously exploit the collaborative and competitive relationships among multiple metrics, where the metrics display unity against the generator for effective distance measurement as well as compete for more training data through a metric discriminator to avoid overlapping. Extensive experimental results on five benchmark datasets show that our DAML and DAMML effectively boost the performance of existing deep metric learning approaches through adversarial learning.

Siamese network-based visual tracking algorithms have achieved excellent performance in recent years, but challenges such as fast target motion, shape and scale variations have made the tracking extremely difficult. The regression of anchor-free tracking has low computational complexity, strong real-time performance, and is suitable for visual tracking. Based on the anchor-free siamese tracking framework, this paper firstly introduces balance factors and modulation coefficients into the cross-entropy loss function to solve the classification inaccuracy caused by the imbalance between positive and negative samples as well as the imbalance between hard and easy samples during the training process, so that the model focuses more on the positive samples and the hard samples that make the major contribution to the training. Secondly, the intersection over union (IoU) loss function of the regression branch is improved, not only focusing on the IoU between the predicted box and the ground truth box, but also considering the aspect ratios of the two boxes and the minimum bounding box area that accommodate the two, which guides the generation of more accurate regression offsets. The overall loss of classification and regression is iteratively minimized and improves the accuracy and robustness of visual tracking. Experiments on four public datasets, OTB2015, VOT2016, UAV123 and GOT-10k, show that the proposed algorithm achieves the state-of-the-art performance.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

As natural language interfaces enable users to express increasingly complex natural language queries, there is a parallel explosion of user review content that can allow users to better find items such as restaurants, books, or movies that match these expressive queries. While Neural Information Retrieval (IR) methods have provided state-of-the-art results for matching queries to documents, they have not been extended to the task of Reviewed-Item Retrieval (RIR), where query-review scores must be aggregated (or fused) into item-level scores for ranking. In the absence of labeled RIR datasets, we extend Neural IR methodology to RIR by leveraging self-supervised methods for contrastive learning of BERT embeddings for both queries and reviews. Specifically, contrastive learning requires a choice of positive and negative samples, where the unique two-level structure of our item-review data combined with meta-data affords us a rich structure for the selection of these samples. For contrastive learning in a Late Fusion scenario (where we aggregate query-review scores into item-level scores), we investigate the use of positive review samples from the same item and/or with the same rating, selection of hard positive samples by choosing the least similar reviews from the same anchor item, and selection of hard negative samples by choosing the most similar reviews from different items. We also explore anchor sub-sampling and augmenting with meta-data. For a more end-to-end Early Fusion approach, we introduce contrastive item embedding learning to fuse reviews into single item embeddings. Experimental results show that Late Fusion contrastive learning for Neural RIR outperforms all other contrastive IR configurations, Neural IR, and sparse retrieval baselines, thus demonstrating the power of exploiting the two-level structure in Neural RIR approaches as well as the importance of preserving the nuance of individual review content via Late Fusion methods.

Contrastive learning is an important technique in the field of machine learning for learning data representations. In the field of self-supervised visual representation learning, the strategy of positive and negative sample selection is key to improving the efficiency and effectiveness of model learning. Traditional self-supervised learning methods often employ random sampling strategies to select positive and negative samples, but this approach may result in uneven sample quality when dealing with complex datasets, thereby affecting the learning outcomes. To alleviate this problem, this study is dedicated to exploring more effective strategies for positive and negative sample selection and processing to optimize the self-supervised learning process. To this end, we propose an improved method of self-supervised learning called contrastive learning with enhanced diversity. On the one hand, this method utilizes the weight parameters of the DINO pre-trained model to initialize the feature extraction network of SimCLR, providing more accurate calculations of feature similarity. On the other hand, by setting a threshold on the feature similarity matrix and penalizing (subtracting 0.5 from) similarity scores that do not exceed this threshold, we reduce the excessive impact of high similarity scores on model training, thereby helping the model better distinguish between positive and negative samples. In downstream image classification tasks, we conducted detailed evaluations of the improved model, specifically including fine-tuning and linear evaluation aspects. Experiments show that the proposed approach improves the performance of the loss functions and improves the accuracy of the proposed SimCLR model.

Efficient attention based deep fusion CNN for smoke detection in fog environment

Many learning techniques have been applied to identify disease-associated genes. At the early, they usually approached this problem as a binary classification, where training set is comprised of positive and negative samples. In which, positive samples are constructed from known disease genes, whereas negative samples are the remaining which are not known to be associated with diseases. This is the limitation of the binary classification-based solutions, since the negative training set should be actual non-disease genes; however, construction of this set is nearly impossible in biomedical researches. Therefore, to reduce this uncertainty, more realistic classification-based methods have been proposed. For instance, unary classification technique based on one-class SVM method was proposed by learning from only positive samples. In addition, the remaining set may contain unknown disease genes; therefore, semi-supervised methods such as binary semi-supervised and positive and unlabeled (PU) learning classifications have been proposed. In particular, PU learning methods, which learn from both known disease genes and the remaining genes, were shown to outperform others. In these studies, data sources are usually represented by vectorial form for binary classifiers, while they are in kernel matrices for unary and PU learning ones. The kernel-based data fusion is only suitable for data with different types and it seems unfair for the comparison based on different data representations. Therefore, in this study, we compared different classification techniques for the disease gene prediction based on vectorial representation of samples. The simulation result showed that the unary classification technique, which combines both density and class probability estimation strategies, achieved the best performance, whereas it is worst for the one-class SVM-based method. Interestingly, performance of the best binary classification technique is comparable with that of biased SVM-based PU learning and binary semi-supervised classification methods. And, they are all better than the multi-level SVM-based one.

This paper presents a focused Web crawling system iSurfer for information retrieval from the Web. Different from other focused crawlers, iSurfer uses an incremental method to learn a page classification model and a link prediction model. It employs an online sample detector to incrementally distill new samples from crawled Web pages for online updating of the model learned. Other focused crawling systems use classifiers that are built from initial positive and negative samples and can not learn incrementally. The performances of these classifiers depend on the topical coverage of the initial positive and negative samples. However, the initial samples, particularly the negative ones, with a good coverage of target topics are difficult to find. Therefore, the iSurfer’s incremental learning strategy has an advantage. It starts from a few positive samples and gains more integrated knowledge about the target topics over time. Our experiments on various topics have demonstrated that the incremental learning method can improve the harvest rate with a few initial samples.KeywordsFocused CrawlerIncremental LearningPositive Sample Based LearningLink PredictionWeb Page Classification

The Covid-19 pandemic has not ended yet. Therefore, laboratory tests are very important for determining the diagnosis of Covid-19. At this case, PCR is the gold standard in laboratory tests. Therefore, the accuracy of the PCR assay should be a major concern. This study aims to evaluate the validation results of several Covid-19 PCR reagent kits. A total of 10 positive and negative clinical samples were used as test samples. DNA extraction was performed using a commercial reagent kit. PCR examination was carried out using four different blinded commercial reagent kits. The results of the examination were compared in each sample. The results showed that Reagents kit A, C, and D gave the same results for each sample, namely five positive and five negative samples. Reagent kit B showed different results, namely eight positive samples and two negative samples. A total of 3 (33.3%) of the 10 sample examinations indicated inaccurate results for Reagent kit B. The Reagent kit B test results using two different boxes showed different results with seven positive results by box 1, while eight positive results by another box. In conclusion, 1 out of 4 tested Covid-19 PCR kit reagents gave inaccurate results. Therefore, in covid-19 testing laboratories, it is recommended to choose a PCR reagent kit that has been approved by a trusted organization and perform reagent validation tests.

Live HIV rapid test kits (RTKs) are usually packaged with only a single test buffer vial. A major challenge with HIV testing is the problem of lack of the RTK buffer vial to complete the number of tests in an RTK pack due to misuse, misplacement or contamination of buffer with clients' blood during HIV testing. This has led to the use of sterile normal saline and tap water as substitute liquids in place of the buffer vial. This study compared the use of the commercially provided buffer vial and these substitute liquids in HIV rapid testing while using the commercially provided buffer as gold standard. One hundred (100) HIV positive samples from patients attending antiretroviral therapy (ART) clinic and 100 HIV negative blood samples from blood donors placed in EDTA were tested with Determine RTK using the commercially provided buffer and substitute liquids (Sterile normal saline and tap water). The Determine RTK was also tested using the commercially provided buffer, normal saline and tap water without addition of blood samples. Results of the tests with substitute liquids were compared with those obtained with that of RTK's commercially provided buffer. With HIV positive and negative blood samples, the expected test line and control line or only control line (for negative samples) were observed with the commercially provided buffer and sterile normal saline. When tested without blood samples using the RTK's buffer and normal saline, the control line was observed but no test line for all 100 tests done. Invalid test results were recorded in 48% of the negative samples and 40% of test with no blood added when with tap water was used. This study also shows varied colour intensity for the buffer vial and substitute liquids used. For samples tested with sterile normal saline, 52% of the tested samples showed stronger colour intensity in test line that control line, 37% had equal colour intensity while 11% had higher colour intensity in control than test line. Seventy seven (77%) of Samples tested with tap water had the colour intensity greater in test line than control line while 23% had equal colour intensity in both test and control line. With sterile normal saline used with HIV positive and negative blood samples, a 100% clear background like that of the gold standard was obtained. However, tap water when used as a substitute buffer to run all 100 HIV positive samples had an unclear background. An unclear background was also observed in 100% and 28% of the test ran with tap water only (without blood samples) and with HIV Negative samples respectively.

Weakly supervised whole slide image (WSI) classification is challenging due to the lack of patch-level labels and high computational costs. State-of-the-art methods use self-supervised patch-wise feature representations for multiple instance learning (MIL). Recently, methods have been proposed to fine-tune the feature representation on the downstream task using pseudo labeling, but mostly focusing on selecting high-quality positive patches. In this paper, we propose to mine hard negative samples during fine-tuning. This allows us to obtain better feature representations and reduce the training cost. Furthermore, we propose a novel patch-wise ranking loss in MIL to better exploit these hard negative samples. Experiments on two public datasets demonstrate the efficacy of these proposed ideas. Our codes are available at https://github.com/winston52/HNM-WSI.

Reliable and rapid detection of severe acute respiratory syndrome coronavirus 2 in laboratory setting is critical to control the pandemic. We aimed to an evaluated polymerase chain reaction (PCR) efficiency of nasopharyngeal swabs stored in viral transport medium (VTM) in different temperatures. Ninety swabs taken into VTM were analyzed at the first hour, then divided into two groups with similar numbers of positive and negative samples. Positive samples of each group were also subgrouped according to Fam CT values as low CT (<25), medium CT (25-32), and high CT (32-38) groups. One group was stored at 4°C, while the other was stored at room temperature, PCR analyses were repeated every 24 h for 5 days and on Day 12. There was a total of 30 positive samples (12 low CT, 11 medium CT, and 7 high CT). The CT values of both groups remained unchanged in first 3 days while the CT values of the room temperature group increased after the third day. All of the positive samples remained positive in both groups for the first 5 days. On the 12th day, the total number of positives decreased to 8 in the room temperature group and 11 in the 4°C groups. All the low CT samples remained positive in both groups. In conclusion, it is safe to store positive samples in room temperature for up to 5 days. Only samples with high viral loads remain positive for 12 days, regardless of whether stored at room temperature or 4°C. Negative samples don't turn to invalid if stored in VTM.