Similar Video Research Articles

An open-access database of video stimuli for action observation research in neuroimaging settings: psychometric evaluation and motion characterization.

Video presentation has become ubiquitous in paradigms investigating the neural and behavioral responses to observed actions. In spite of the great interest in uncovering the processing of observed bodily movements and actions in neuroscience and cognitive science, at present, no standardized set of video stimuli for action observation research in neuroimaging settings exists. To facilitate future action observation research, we developed an open-access database of 135 high-definition videos of a male actor performing object-oriented actions. Actions from 3 categories: kinematically natural and goal-intact (Normal), kinematically unnatural and goal-intact (How), or kinematically natural and goal-violating (What), directed toward 15 different objects were filmed from 3 angles. Psychometric evaluation of the database revealed high video recognition accuracy (Mean accuracy = 88.61 %) and substantial inter-rater agreement (Fleiss' Kappa = 0.702), establishing excellent validity and reliability. Videos' exact timing of motion onset was identified using a custom motion detection frame-differencing procedure. Based on its outcome, the videos were edited to assure that motion begins at the second frame of each video. The videos' timing of category recognition was also identified using a novel behavioral up-down staircase procedure. The identified timings can be incorporated in future experimental designs to counteract jittered stimulus onsets, thus vastly improving the sensitivity of neuroimaging experiments. All videos, their psychometric evaluations, and the timing of their frame of category recognition, as well as our custom programs for performing these evaluations on our, or on other similar video databases, are available at the Open Science Framework (https://osf.io/zexc4/).

Similarity-based ranking of videos from fixed-size one-dimensional video signature

The amount of information is multiplying, one of the popular and widely used formats is short videos. Therefore, maintaining the copyright protection of this information, preventing it from being disclosed without authorization, is a challenge. This work presents a way to rank a set of short videos based on a video profile similarity metric, finding a set of reference videos, using a self-supervised method, without the need for human tagging. The self-supervised method uses a search based on a Genetic Algorithm, of a subgroup of the most similar videos. Similarities are calculated using the SMAPE metric on video signatures vectors, generated with a fixed size, using Structural Tensor, maximum sub matrix and T-SNE.

Lessons learned from naturalistic driving data processing in a secure data enclave: Preliminary discoveries from analyzing dash camera videos

This paper provides preliminary insights on the challenges of processing Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) videos and data, particularly those with Personally Identifiable Information (PII). Insights and lessons learned are presented from a study designed to evaluate the effectiveness of High Visibility Crosswalks (HVCs). Over a one-month period, 15,379 videos were processed in the secure data enclave of Virginia Tech Transportation Institute (VTTI). As these videos are not available outside of the secure data enclave due to PII restrictions, researchers visiting the secure data enclave for the first time may face several challenges: navigating the software interface; identifying the video views and frames of interest; and identifying and extracting information of interest from the video views, etc. These challenges, the procedures followed to address them, and the process for identifying and classifying distracted driving behaviors are discussed. Lastly, hypothesis tests are conducted to investigate distracted driving behavior, with the results revealing that HVCs have the potential to make drivers more cautious in their proximity. The information presented in this paper is expected to aid researchers who intend to utilize SHRP2 NDS or similar videos for future research, to preemptively plan for the video processing phase.

Interobserver agreement of an ED PoCUS video training dataset of normal appendix and appendicitis in children

BackgroundEducational video datasets can be an effective method for training in emergency department (ED) point-of-care ultrasound (PoCUS). A video dataset for normal appendix and appendicitis in children using ED PoCUS images was developed to assess interobserver agreement, as measured by Cohen’s Kappa on key sonographic findings.MethodsThree sets of 25 ED PoCUS videos were selected and curated from pediatric patients with normal appendix and acute appendicitis. Four participant ED sonologist-physicians were trained on the first set of 25 videos showing normal appendix or normal bowel in patients without appendicitis to note if normal appendix was seen in any part or in it’s entirety from tip-to-cecum. They were then tested on the second set of similar videos. A third set of 25 videos from patients who had appendicitis where participant sonologists were asked to note if appendicitis was present or absent, with and without appendicolith or perforation. Cohen’s Kappa was calculated in aggregate and stratified by experience vs. novice against a senior sonologist-physician aware of all patient outcomes for visualization of: 1. any part of normal appendix, 2. normal appendix visualized from tip to cecum 3. any part of appendicitis, 4. appendicolith, 5. appendiceal perforation.ResultsCohen’s Kappa for any part of normal appendix, 0.71, 95% CI (0.58–0.85); normal appendix tip-to-cecum, 0.43, 95% CI (0.19–0.67), appendicitis, 0.53, 95%CI (0.34–0.70), appendicolith, 0.63, 95%CI (0.43–0.84), perforated appendicitis, 0.46, 95%CI (0.22–0.70). Stratified by experienced vs. novice: any part of normal appendix, 0.75 vs. 0.68; normal appendix tip-to-cecum, 0.50 vs. 0.36; appendicitis, 0.78 vs. 0.31; appendicolith, 0.75 vs. 0.5; perforated appendicitis, 0.5 vs 0.42.ConclusionsThis educational video dataset may be used to train sonologist-physicians in ED PoCUS scanning for normal appendix and appendicitis in children. Sonologist experience affected interobserver agreement with respect to visualization of entire normal appendix and appendicitis.

Implementation of video assisted point-of-care germline genetic education and testing for patients with peri-diagnostic pancreatic cancer.

e22530 Background: Germline genetic testing is universally recommended for patients with exocrine pancreatic cancer (PC). Pretest consultation with a genetics professional is recommended to ensure patients are informed about risks, benefits, and limitations of testing. This is a resource intensive process and is associated with a suboptimal testing rate (~30%). Prior work demonstrates that video-assisted pre-test education does not increase patient anxiety compared to traditional pre-test genetic counseling in peridiagnostic ovarian cancer patients. In this report, we assess the feasibility of using a similar video assisted, streamlined genetic education and testing approach in peridiagnostic PC patients. Methods: As part of a pilot quality improvement initiative, patients with a diagnosis of exocrine PC seen in a single academic practice were offered streamlined, video-assisted genetic education followed by germline testing. Patient eligibility for this pilot included: new diagnosis or radiographic suspicion of PC; English language proficiency; and no prior germline testing. After this approach was presented by the medical oncology team, eligible patients viewed a 9-minute educational video describing the goals, pros, and cons of germline genetic testing. After watching the video, patients were given the option to proceed with testing that day. Patients with pathogenic mutations were referred for formal post-test genetic counseling, and patients with variants of uncertain significance (VUSs) were referred on a case-by-case basis. Following IRB approval, we conducted a retrospective review of the experience of all patients presenting to this pilot site from April-Dec 2023. Results: During the study period, 56 new patients with exocrine PC presented to the pilot site. 50 patients were eligible for participating in the pilot program. All patients, except one who declined on the basis of transferring care, consented to testing (49/50; 98%). Median patient age was 71, 49% were female. Race distribution was: White (30), Asian (6), Black (4), Other (2), Declined (7). Median time from patient diagnosis to testing was 11 days. One patient had a germline ATM mutation, and this patient was seen for formal posttest counseling 18 days after testing was initiated. Seven patients had VUSs. Six new patients presenting during the study period were not eligible for this pilot, all due to limited English proficiency. All 6 of these patients underwent genetic testing after translation service assisted counseling by either the primary oncology or cancer genetics team. Conclusions: Video assisted point-of-care genetics education and testing led to nearly universal uptake of genetic testing in peridiagnostic PC patients. This workflow was feasible, timely, and optimized use of genetic counseling resources.

Characteristics of popular diet-related TikTok videos

TikTok is a global social media platform popular with young people, encouraging viewers to engage with user-generated video content (through likes, shares and comments) and mimic displayed behaviours (by creating similar videos or content). It is therefore a powerful and engaging tool that has potential for the widespread dissemination of information (or misinformation) and to influence behaviours. Social media is saturated with the promotion of ‘diet culture’ and has a strong influence on the eating behaviours of young people (1,2). This is concerning as childhood and adolescence are critical periods of growth requiring optimal nutrition (3). This study aimed to investigate how dieting is portrayed on TikTok and consider the potential implications for public health. A cross-sectional descriptive content analysis was undertaken of 50 videos from each of the five most popular diet-related hashtags (#diet, #whatieatinaday, #wieiad, #dietitian, #diettips). A codebook was developed to analyse the body-related content referenced (weight measurement, calories, body image, body checking, body comparisons over time) and collect the engagement for each video (likes, comments and shares). Overall, the 250 videos contributed to a total of 197.2 million likes (M = 789,051.0, SD = 923,335.0), 126,7251 comments (M = 5,069.0, SD = 9,315.3), and 3.8 million shares (M = 15,423.2, SD = 41,271.3). Most videos featured adults (n = 205, 82%), and only a small number of videos featured identifiable children or adolescents (n = 16, 6.4%). The information and advice regarding diets and eating behaviours came from two types of sources: 'experts’ and regular creators sharing their personal experience. Almost half of the videos were educational or instructional (n = 116, 46.4%), of which 74 (63.8%) were posted by users who claimed expert status. The most common titles by those claiming to be experts were “dietitian” (n = 43, 58.1%), “health and wellness coach” (n = 7, 9.5%), “doctor” (MD; n = 6, 8.1%), “fitness trainer” (n = 6, 8.1%), and “nutritionist” (n = 3, 4.1%). Of the videos that specifically mentioned a certain diet, the most common diets were low calorie (n = 32, 12.8%), vegan (n = 14, 5.6%), self-reported healthy eating (n = 12, 4.8%), and fasting (n = 10, 4.0%). The use of humour was the only feature significantly associated with engagement. Health professionals are presented with a unique opportunity to widely and positively influence the public by utilising the popularity of TikTok amongst young people to disseminate evidence-based information and promote healthy eating behaviours. Our findings suggest that experts using authentic, humorous, and engaging personas would be more likely to be popular on this platform.

Abstract 6375: Genetic testing station increases access to germline genetic testing

Abstract Despite evidence of clear benefit, genetic testing of pediatric oncology patients for germline variants remains rare due to a variety of factors, including insurance/financial barriers, limited knowledge about genetic testing, and competing interests for time and resources. Limited access to genetic testing, pre-test counseling and multidisciplinary care have been shown to disproportionately affect patients with limited resources. This represents a critical unmet need in pediatric oncology. Genetic testing station (GTS) is a pre-test counseling video tool that reviews what genetic testing is, why it might be indicated, and explains potential results. Similar video tools have been successfully used in other settings to increase rates of genetic testing, but there are no published reports on the effectiveness of a video education intervention to increase genetic testing in pediatric oncology patients. To test the effectiveness of GTS on genetic testing knowledge and feelings about genetic testing compared to in-person counseling, we use 5 previously validated tools pre- and post-intervention. The initial surveys measure genetic testing knowledge, decisional conflict, healthy literacy, evaluation of GTS and demographics. If participants complete genetic testing, we use the MICRA to assess the impact on their cancer risk assessment. Our GTS arm is recruited from UCLA Pediatric Sarcoma patients, and the in-person counseling arm is recruited from the UCLA Cancer Predisposition clinic. We have recruited 28 participants in the GTS arm and seven in the in-person counseling arm, of which 10 are patients over 18 years old, two are patients between 12-17 years old, and 23 are parents. In our GTS arm, there are 20 female and eight male participants. A majority identify as white or Latino. For the in-person counseling arm, there are four male and three female participants who identify as white, Middle Eastern, or Latino. A similar percentage score with high health literacy, 68% in the GTS arm and 71% for the in-person counseling arm. The majority of patients have no decisional conflict pre- or post-intervention for the GTS group, while the in-person counseling arm shows high initial decision conflict that is fully resolved with counseling. There is a statistically significant increase in genetic testing knowledge for five out of 10 questions comparing pre- and post-GTS. For GTS participants who have completed genetic testing and the MICRA, there is overall low distress and uncertainty, but higher scores for parents than adult or minor patients. In conclusion, there has been high interest in germline genetic testing in pediatric oncology patients with all families deciding to pursue genetic testing after viewing GTS. GTS is an effective tool in increasing genetic testing knowledge in participants. Therefore, GTS is a promising approach to increase access to genetic testing, while providing adequate pre-test counseling and no decisional conflict. Citation Format: Camille Hamilton, Mariana Niell Swiller, Franceska Bhansali, Alyssa Hoover, Stephanie Bottomley, Marisa Lewis, Bianca Russell, Julian A. Martinez, Beth Karlan, Noah Federman, Vivian Y. Chang. Genetic testing station increases access to germline genetic testing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6375.

TherapyTok for Depression and Anxiety: A Quantitative Content Analysis of High Engagement TikTok Videos

PurposeTikTok is increasingly becoming a source of health information, peer support, and validation regarding mental health. The goal of this study was to analyze the content of TikTok videos related to depression and anxiety. MethodsThe sample included 100 videos, each with at least a million views, discussing either depression, anxiety, or both. The videos were retrieved from hashtag searches. The videos were coded for the type of mental health condition; specific content being discussed (e.g., symptoms or treatment); video presentation (i.e., personal experience, expert information, or general discussion), and more subcategories. Engagement statistics (i.e., likes, views, reposts, and number of comments) for each video were also recorded. ResultsThe engagement statistics were higher for personal experience videos than for videos from healthcare professionals. Anxiety was the subject of 57% of the videos irrespective of the search hashtags, and over two-thirds of the videos were created by females. The most discussed topics included the description or enactment of depression/anxiety symptoms (e.g., emotional displays); mention of being diagnosed by a healthcare professional was the least prevalent. DiscussionThese results suggest that depression and anxiety videos featuring personal experiences are prevalent on TikTok with higher engagement compared to similar videos by health professionals. The attribution of generic symptoms to these mental health conditions may result in self-diagnosis. There is a need for more strategic efforts to ensure quality of health content on TikTok and increased focus on digital health literacy to make young social media users critical consumers of online content.

Multi-Modal Deep Learning based Metadata Extensions for Video Clipping

General video search and recommendation systems primarily rely on metadata and personal information. Metadata includes file names, keywords, tags, and genres, among others, and is used to describe the video's content. The video platform assesses the relevance of user search queries to the video metadata and presents search results in order of highest relevance. Recommendations are based on videos with metadata judged to be similar to the one the user is currently watching. Most platforms offer search and recommendation services by employing separate algorithms for metadata and personal information. Therefore, metadata plays a vital role in video search. Video service platforms develop various algorithms to provide users with more accurate search results and recommendations. Quantifying video similarity is essential to enhance the accuracy of search results and recommendations. Since content producers primarily provide basic metadata, it can be abused. Additionally, the resemblance between similar video segments may diminish depending on its duration. This paper proposes a metadata expansion model that utilizes object recognition and Speech-to-Text (STT) technology. The model selects key objects by analyzing the frequency of their appearance in the video, extracts audio separately, transcribes it into text, and extracts the script. Scripts are quantified by tokenizing them into words using text-mining techniques. By augmenting metadata with key objects and script tokens, various video content search and recommendation platforms are expected to deliver results closer to user search terms and recommend related content.

SARS-CoV-2 specific sIgA in saliva increases after disease-related video stimulation

Secretory immunoglobulin A (sIgA) in saliva is the most important immunoglobulin fighting pathogens in the respiratory tract and may thus play a role in preventing SARS-CoV-2 infections. To gain a better understanding of the plasticity in the mucosal antibody, we investigated the proactive change in secretion of salivary SARS-CoV-2-specific sIgA in 45 vaccinated and/or previously infected, generally healthy persons (18 to 35 years, 22 women). Participants were exposed to a disease video displaying humans with several respiratory symptoms typical for COVID-19 in realistic situations of increased contagion risk. The disease video triggered an increase in spike-specific sIgA, which was absent after a similar control video with healthy people. The increase further correlated inversely with revulsion and aversive feelings while watching sick people. In contrast, the receptor binding domain-specific sIgA did not increase after the disease video. This may indicate differential roles of the two salivary antibodies in response to predictors of airborne contagion. The observed plasticity of spike-specific salivary antibody release after visual simulation of enhanced contagion risk suggests a role in immune exclusion.

First-Person Video Domain Adaptation With Multi-Scene Cross-Site Datasets and Attention-Based Methods

Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person video action recognition is an under-explored problem, with a lack of benchmark datasets and limited consideration of first-person video characteristics. Existing benchmark datasets provide videos with a single activity scene, e.g. kitchen, and similar global video statistics. However, multiple activity scenes and different global video statistics are still essential for developing robust UDA networks for real-world applications. To this end, we first introduce two first-person video domain adaptation datasets: ADL-7 and GTEA_KITCHEN-6. To the best of our knowledge, they are the first to provide multi-scene and cross-site settings for UDA problem on first-person video action recognition, promoting diversity. They provide five more domains based on the original three from existing datasets, enriching data for this area. They are also compatible with existing datasets, ensuring scalability. First-person videos have unique challenges, i.e. actions tend to occur in hand-object interaction areas. Therefore, networks paying more attention to such areas can benefit common feature learning in UDA. Attention mechanisms can endow networks with the ability to allocate resources adaptively for the important parts of the inputs and fade out the rest. Hence, we introduce channel-temporal attention modules to capture the channel-wise and temporal-wise relationships and model their inter-dependencies important to this characteristic. Moreover, we propose a Channel-Temporal Attention Network (CTAN) to integrate these modules into existing architectures. CTAN outperforms baselines on the new datasets and one existing dataset, EPIC-8.

Caregiver Psychological Maltreatment Behaviors Toward Children on TikTok.

We examined TikTok user engagement when parents post videos engaging in psychological maltreatment (PM) behaviors towards their children, using the APSAC-endorsed definition of PM. A new TikTok account was created and seeded with PM behavior videos identified previously; similar videos then appeared on the new account's "For You Page" (an algorithmic feed curated by TikTok). Researchers identified 35 creators who had posted at least one PM behavior video, resulting in their full profile being coded (N = 2684 videos) for five engagement metrics, presence of children, and presence of PM behavior. Non-parametric paired comparisons (Mann-Whitney tests) were made within individual creators for: (1) engagement metrics before and after the first PM behavior video, (2) engagement metrics for PM behavior videos versus non-PM videos, (3) engagement metrics for child videos versus non-child videos, and (4) proportion of videos containing children before and after the first PM video. All but one analysis was significant (effect sizes from .28 to .59, average r = .46). We discuss directions for future research, as well as how child welfare and content moderation policy can be updated to change social norms around sharenting.

Attention-based automatic editing of virtual lectures for reduced production labor and effective learning experience

Recently there has been a surge in demand for online video-based learning, and the importance of high-quality educational videos is ever-growing. However, a uniform format of videos that neglects individual differences and the labor-intensive process of editing are major setbacks in producing effective educational videos. This study aims to resolve the issues by proposing an automatic lecture video editing pipeline based on each individual’s attention pattern. In this pipeline, the eye-tracking data are obtained while each individual watches virtual lectures, which later go through multiple filters to define the viewer’s locus of attention and to select the appropriate shot at each time point to create personalized videos. To assess the effectiveness of the proposed method, video characteristics, subjective evaluations of the learning experience, and objective eye-movement features were compared between differently edited videos (attention-based, randomly edited, professionally edited). The results showed that our method dramatically reduced the editing time, with similar video characteristics to those of professionally edited versions. Attention-based versions were also evaluated to be significantly better than randomly edited ones, and as effective as professionally edited ones. Eye-tracking results indicated that attention-based videos have the potential to decrease the cognitive load of learners. These results suggest that attention-based automatic editing can be a viable or even a better alternative to the human expert-dependent approach, and individually-tailored videos have the potential to heighten the learning experience and effect.

Blind video watermarking scheme for medical video authentication

Medical video watermarking is one of the beneficial and efficient tools to prohibit important patients' data from illicit enrollment and redistribution. In this paper, a new blind watermarking scheme has been proposed to improve the confidentiality, integrity, authenticity, and perceptual quality of a medical video with minimum distortion. The proposed scheme is based on 2D-DWT and dual Hessenberg-QR decomposition, where the input medical video is initially processed into frames. Then, the processed frames are transformed into sub-bands using 2D-DWT, followed by applying Hessenberg-QR decomposition on the selected wavelet HL2 sub-band. The watermark is scrambled via Arnold cat map to raise confidentiality and then concealed in the modified selected features. The watermark is extracted in a fully blind mode without referencing the original video, which reduces the extraction time. The proposed scheme maintained a fundamental tradeoff between robustness and visual imperceptibility compared to existing methods against many commonly encountered attacks. The visual imperceptibility has been evaluated using well-known metrics PSNR, SSIM, Q-index, and histogram analysis. The proposed scheme achieves a high PSNR value of (70.6899 dB) with minimal distortion and a high robustness level with an average NC value of (0.9998) and BER value of (0.0023) while conserving a large payload capacity. The obtained results show superior performance over similar video watermarking methods. The limitation of this scheme is the elapsed time during the embedding process since we utilized dual Hessenberg-QR decomposition. One possible solution to reduce time consumption is simple decompositions like bound-constrained SVM or similar decompositions.

Show don’t tell: assessing the impact of co-developed patient information videos in paediatric uveitis

Background/ObjectivesThere is a paucity of online educational content targeting children and young people with uveitis. We evaluated the impact of a co-designed patient education video on subjective and objective understanding of childhood uveitis.Subjects/MethodsCo-designed patient education media were produced in collaboration with the Childhood Uveitis Studies steering group and the Great Ormond Street Hospital Generation R Young People’s Advisory Group and narrated by children. Patients managed within the Uveitis service at GOSH were invited to take part in a pre–post survey, undertaken immediately prior to and following viewing of a patient education video.ResultsForty-three patients participated. These were stratified according to age, duration of disease, and treatment type for analysis. Self-rated knowledge improved across all groups (p = 0.001), particularly in those with a new diagnosis of uveitis (Z = −8.124, p < 0.001). Objective knowledge scores improved across all questions, especially in younger children, those with new disease, and those on steroid only treatment (Z = −3.847, p < 0.001, Z = −3.975, p < 0.001, Z = −3.448, p < 0.001; respectively). Most participants reported the videos to be easy to understand and with the right amount of information. All stated that they learned something new.ConclusionsPatient understanding of disease and treatment is crucial to achieving the best possible outcomes for this chronic, relapsing remitting and potentially blinding disorder. Our findings data shows the potential value of co-designed patient information videos, specifically in our study benefitting younger patients and those recently diagnosed. We suggest that other clinical teams could collaborate fruitfully with patient groups to develop similar videos to target possible misinformation and potentially improve patient outcomes.

When pretesting corporate social responsibility advertising can mislead: feelings vs attitudes

PurposeThe main purpose of this study is to contribute to the literature concerned with improving the effectiveness of corporate social responsibility (CSR) advertising by considering how such ads are pre-tested.Design/methodology/approachTwo similar video ads were produced: one using an informative appeal and the other using an emotional appeal. The latter appeal is more widely used by practitioners. Each ad was designed to promote the CSR credentials of the same (fictitious) company. A web-based experiment (n = 244) was used to test both using two types of measure: first attitude towards the company (such as its image) and second the feelings evoked by the ad.FindingsAs predicted from theory, the ads promoted similar evaluations of the company but the evaluations measured by evoked feelings differed significantly. The information-based ad evoked more positive emotions, less negative emotions and more positive attitudes toward the ad. If the ads had been pretested using only measures of evoked feelings, the more emotive treatment would have been rejected.Practical implicationsThe study shows why CSR ads should be pretested and why such tests should include multiple measures. It also illustrates how informative CSR video ads can be better received but how both informative and emotional appeals can be used when communicating a company's CSR.Originality/valueThere is little research relevant to the pretesting of ads designed to communicate a company's CSR. Signaling theory can help explain why comparable (CSR) video ads can be evaluated as similar in their effect on company related evaluations.

Attentive spatial-temporal contrastive learning for self-supervised video representation

Most existing self-supervised works learn video representation by using a single pretext task. A single pretext task, providing single supervision from unlabeled data, may neglect to describe the difference between spatial features and temporal features. The similar spatial features and temporal features may hinder distinguishing between two similar videos with different class labels. In this paper, we propose an attentive spatial–temporal contrastive learning network (ASTCNet), which learns self-attention spatial–temporal features by contrastive learning between multiple spatial and temporal pretext tasks. The spatial features are learned by multiple spatial pretext tasks, including spatial rotation, and spatial jigsaw. Each spatial feature is enhanced with spatial self-attention by learning the relation between patches. The temporal features are learned by multiple temporal pretext tasks, including temporal order, and temporal pace. Each temporal feature is enhanced with temporal self-attention by learning the relation between frames, and is enhanced by feeding the optical flow features into a motion module. To separate the spatial feature and the temporal feature learned in one video, we represent the video as different features for each pretext task, and design pretext task-based contrastive loss. The pretext task-based contrastive loss encourages the different pretext tasks to learn dissimilar features, and encourages the same pretext task to learn similar features. The pretext task-based contrastive loss can learn the discriminative features for each pretext task in one video. The experiments show that our method achieves state-of-the-art performance for self-supervised action recognition on the UCF101 dataset and the HMDB51 dataset.

Vision Transformer-Based Video Hashing Retrieval for Tracing the Source of Fake Videos

With the increasing negative impact of fake videos on individuals and society, it is crucial to detect different types of forgeries. Existing forgery detection methods often output a probability value, which lacks interpretability and reliability. In this paper, we propose a source-tracing-based solution to find the original real video of a fake video, which can provide more reliable results in practical situations. However, directly applying retrieval methods to traceability tasks is infeasible since traceability tasks require finding the unique source video from a large number of real videos, while retrieval methods are typically used to find similar videos. In addition, training an effective hashing center to distinguish similar real videos is challenging. To address the above issues, we introduce a novel loss function, hash triplet loss, to capture fine-grained features with subtle differences. Extensive experiments show that our method outperforms state-of-the-art methods on multiple datasets of object removal (video inpainting), object addition (video splicing), and object swapping (face swapping), demonstrating excellent robustness and cross-dataset performance. The effectiveness of the hash triplet loss for nondifferentiable optimization problems is validated through experiments in similar video scenes.

Neural similarity between overlapping events at learning differentially affects reinstatement across the cortex

Episodic memory often involves high overlap between the actors, locations, and objects of everyday events. Under some circumstances, it may be beneficial to distinguish, or differentiate, neural representations of similar events to avoid interference at recall. Alternatively, forming overlapping representations of similar events, or integration, may aid recall by linking shared information between memories. It is currently unclear how the brain supports these seemingly conflicting functions of differentiation and integration. We used multivoxel pattern similarity analysis (MVPA) of fMRI data and neural-network analysis of visual similarity to examine how highly overlapping naturalistic events are encoded in patterns of cortical activity, and how the degree of differentiation versus integration at encoding affects later retrieval. Participants performed an episodic memory task in which they learned and recalled naturalistic video stimuli with high feature overlap. Visually similar videos were encoded in overlapping patterns of neural activity in temporal, parietal, and occipital regions, suggesting integration. We further found that encoding processes differentially predicted later reinstatement across the cortex. In visual processing regions in occipital cortex, greater differentiation at encoding predicted later reinstatement. Higher-level sensory processing regions in temporal and parietal lobes showed the opposite pattern, whereby highly integrated stimuli showed greater reinstatement. Moreover, integration in high-level sensory processing regions during encoding predicted greater accuracy and vividness at recall. These findings provide novel evidence that encoding-related differentiation and integration processes across the cortex have divergent effects on later recall of highly similar naturalistic events.

Unsupervised Video Segmentation using Quantization

Abstract: Unsupervised video segmentation is a challenging task in computer vision that involves dividing a video into meaningful segments without any labeled data or prior knowledge of the video content. One approach to achieving this is the use of quantization, which involves clustering similar image patches or video frames into discrete groups based on their visual features.Beta-VAE is a type of variational autoencoder (VAE) that is capable of learning disentangled representations of data. It can be applied to video segmentation using quantization, allowing for more effective segmentation and analysis of complex video datasets.The use of Beta-VAE and quantization is essential for several reasons. Firstly, it allows for the automatic analysis of large video datasets without the need for manual annotation, which can be time-consuming and expensive. Secondly, it enables the detection and tracking of objects and events in videos, which has applications in surveillance, robotics, and autonomous driving. Finally, it can be used for content-based video indexing and retrieval, which is crucial for video search and recommendation systems.The advantage of using Beta-VAE in video segmentation is that it can learn disentangled representations of video frames, which separates the data into meaningful factors of variation. This leads to more accurate segmentation, as the model can distinguish between different objects and events in the video, even if they have similar visual features.In conclusion, the use of Beta-VAE and quantization is necessary for advancing the field of computer vision and improving the analysis and understanding of videos. It has numerous applications in various industries and can contribute to the development of more effective and efficient video analysis tools.