21 results about "Instructional video" patented technology

The application discloses a teaching video super-resolution reconstruction method and system based on cross-modal feature fusion, and belongs to the field of computer vision. In view of the problems of low resolution, detail loss, inter-frame jitter and large amount of calculation of the existing teaching video, the application obtains a low-resolution video frame and G-buffer information thereof; in the encoding stage, the cross-channel feature interaction rearrangement and point-by-point product are used to obtain the encoding feature; in the decoding stage, the encoding feature and the G-buffer information are taken as the SS2D module of the Mamba network to perform global space-time feature extraction, and the point-by-point product fusion of the decoding output of the previous frame and the current frame SS2D output is performed; finally, the sub-pixel convolution upsampling is performed to generate a high-resolution frame. The application fuses multi-modal physical information, significantly improves the reconstruction clarity and timing stability of key elements such as board writing and teaching aids, has small parameter quantity and low delay, supports real-time processing, reduces privacy risk based on low-resolution reconstruction, and is suitable for teaching video enhancement and analysis scenes.

Apparatuses, systems, and techniques are presented for generating instructional text. In at least one embodiment, an instructional video is analyzed to determine logical steps of a process or task demonstrated in that video, and instructive text is generated for those logical steps.

The application relates to a driving auxiliary teaching method and device, electronic equipment and a storage medium. The method provided in the application comprises the following steps: acquiring environmental sensing data around a target vehicle and reference driving track data corresponding to a driving route of the target vehicle; determining driving reference data for indicating safe driving of the target vehicle along the reference driving track according to the data; determining driving gap data according to driving behavior data of the target vehicle and the driving reference data; and generating corresponding driving teaching video according to the driving reference data and the driving gap data. By comparing the driving behavior data and the driving reference data, the error driving operation of the user is quantified as the driving gap data, the error correction rate of the driving behavior of the student is improved, meanwhile, the driving gap data is converted into intuitive visual experience through the personalized teaching video, the intuitiveness and attraction of the teaching are greatly improved, and the teaching effect is greatly improved.

This application provides a time-slice intelligent annotation method for knowledge tags in medical teaching videos, relating to the field of video data processing and annotation. It includes: acquiring medical teaching video parsing information, which includes audio-text information, screen content information, and teacher instruction / gesture information; determining the current base time slice as a time slice to be filled based on the audio-text information, wherein the time slice to be filled is one with referential explanations but without a fully identified explicit medical knowledge tag; determining the screen instruction area in the time slice to be filled based on the teacher instruction / gesture information, and extracting medical clues from the area; associating and matching the regional medical clues with preceding medical knowledge tags to determine the implicit medical knowledge tags corresponding to the time slice to be filled; binding the implicit medical knowledge tags with the time slice to be filled to generate time-slice annotation results; thereby reducing the omission of medical knowledge tags in medical teaching videos due to teacher referential explanations.

Slot-level object placement by a robot can be implemented through learning from one demonstration video and one image of the object in the robot perspective. A modular system can be used to tackle this problem while alleviating the need for additional teaching video data, by using a slot-level placement detector. The slot-level detector can generate two-dimensional masks to segment slot masks in the placement container and to allow transformations from the demonstration video perspective to a robot perspective. The resulting transformation can be used by the robot to place one or more objects in slots of the placement object.

This application provides a video processing method, apparatus, electronic device, and computer-readable storage medium, relating to the field of information-based teaching. The method includes: displaying a first action-based teaching video and a first control; in response to the first control being triggered, recording real-time learning actions while the first action-based teaching video is playing, so as to simultaneously play the first action-based teaching video and the real-time learning video, wherein the first action-based teaching video is a first intelligent teaching video or a first intelligent teaching-specific video; the first intelligent teaching-specific video is a video generated based on a second intelligent teaching video and historical learning videos. This application embodiment enables learners to record actions while watching videos and receive real-time corrective feedback, which can significantly improve learning efficiency and enhance user experience.

The application discloses a teaching interaction method and device, electronic equipment and computer storage medium. The method comprises the following steps: receiving an online class listening request sent by a student end; in response to the online class listening request, verifying whether a student corresponding to the student end is currently in a leave state; if the student corresponding to the student end is currently in the leave state, confirming that the online class listening request is verified; under the condition that the online class listening request is verified, allowing the student end to join an online classroom currently opened by a teacher end; the online classroom is used for displaying offline course videos received by the teacher end. In this way, students who can listen to offline classes can also use the student end to listen to online classes, so that they do not pay attention to offline classes due to excessive dependence on online teaching videos, and the learning needs of students who cannot listen to offline classes can be met, so that they can learn offline teaching content through the student end and classmates learning offline.

The application provides a digital surgery room system, comprising: a live broadcast module for conducting demonstration live broadcast and automatically generating a surgery teaching video according to live broadcast content; a remote surgery intervention module for controlling a surgery robot according to received remote surgery instructions to realize remote surgery intervention; and an information collaboration module for remote multidisciplinary consultation, intraoperative medical data calling and pre-hospital emergency remote collaboration.

The present application relates to the technical field of kitchen appliances, and particularly relates to a cooking teaching video processing method and device, equipment and a storage medium, the method comprises the following steps: in response to a cooking instruction carrying cooking video information, determining cooking demonstration video data corresponding to the cooking video information, the cooking demonstration video data comprises a plurality of demonstration cooking action segments; in the cooking process of a user, obtaining cooking image data of the cooking process; performing action feature recognition on the cooking image data to obtain cooking action information; matching and analyzing the cooking action information with each demonstration cooking action segment of the cooking demonstration video data to obtain a target demonstration cooking action segment; and controlling a cooking device to play the target demonstration cooking action segment in a loop until the cooking action information is updated. Through the above method, the target demonstration cooking action segment can be positioned in real time according to the current cooking behavior of the user, and the user experience is significantly improved.

The application discloses a kind of based on document content's explanation video automation generation method and system, it is related to online education and multimedia automation generation technical field.The method will source document to the production flow of teaching video be divided into three core stages of outline generation, HTML and beat sequence joint generation, splicing and check, and four automatic construction steps of TTS dubbing and time axis construction, browser automation screen recording, audio and video synthesis, form complete end-to-end pipeline.The application solves the problems of low production efficiency, sound and picture out of synchronization, fixed style and unreasonable content layout of traditional teaching video production by HTML and beat sequence joint generation mechanism, three-layer sound and picture synchronization architecture, multi-style animation engine, multi-style theme system and multi-level canvas filling adaptive rules, shortens the production cycle from several days to several weeks to minute level, while ensuring the animation expressiveness, style customizability and cross-platform rendering consistency of the video.

This invention relates to a multi-agent teaching video construction method based on code mediation, comprising: S1: acquiring teaching intention input and generating a layered teaching script; using an agent chain framework combined with a cognitive load balancing algorithm, dividing the layered teaching script into page-level unit sequences; S2: converting the page-level unit sequences into executable visual code through a code generation agent; S3: calling a multimedia fusion module to perform closed-loop quality verification and self-repair on the executable visual code, wherein the closed-loop quality verification includes at least audio-visual synchronization calibration, code logic self-healing, and visual layout conflict detection; S4: rendering the repaired executable visual code into a video stream and merging it with the synthesized audio stream to output the final teaching video. The code script of the executable visual code provides educators with a structured editing interface, allowing fine-tuning of video content to be completed simply by modifying parameters, without the need for retraining or large-scale model running.

A method and an apparatus for displaying a teaching video task, a device, a storage medium and a product are provided in embodiments of the present disclosure. The method includes: displaying a triggering control corresponding to a teaching video page on a display interface; and in response to a triggering operation by a user on the triggering control, jumping to the teaching video page, where the teaching video page includes at least one teaching video and at least one target task corresponding to the teaching video. In this way, a user can view and trigger a target task associated with the teaching video more intuitively. Before watching a teaching video, the user can learn of at least one target task corresponding to the teaching video, thereby guiding the user to learn the teaching video better.

The present disclosure provides a deep learning-based video content automatic label generation method, which comprises time sequence segmentation processing of an input teaching video, extraction of basic features of each video segment, and construction of a standardized segment feature sequence; a pre-trained deep neural network is used to determine the semantic dominant object of each video segment based on the segment feature sequence to generate a dominant object label sequence; a multi-level semantic segmentation is performed on the video timeline to obtain a series of semantic units with consistent internal dominant objects and continuous semantics; for each semantic unit, its teaching activity label is predicted, and the label is bound and output with the corresponding semantic dominant object and time interval. The method can accurately perceive and explicitly process the frequent switching of semantic dominant objects in the teaching video, and solve the defects of mixed label semantics and inability to accurately reflect the real structure of the teaching process caused by neglecting subject transformation.

The application discloses a video learning emotion recognition method based on emotion infection tracking and multi-modal fusion, which comprises the following steps: 1) data collection; 2) data preprocessing; 3) feature structure enhancement; 4) emotion infection driven feature enhancement and emotion recognition; 5) testing and evaluation. This method uses eye movement physiological signals to model the bias modulation of individual differences and emotional feedback of learners, and uses it as a guide to track the teaching video inducing factors and infect the feature weighting, and then realizes the deep enhancement fusion of physiological response and video features through the one-way emotion infection tracking module and the two-way cross-modal attention mechanism, finally improves the precision and robustness of emotion recognition.

The application discloses a layered enabling foreign language teaching video anchor point interaction method, and at least two layers of videos can be recorded when a teaching video is recorded, a first layer is a main teaching video, and a second layer is an auxiliary teaching video; wherein, at least a part of shallow knowledge points can be omitted in the main teaching video, and deep knowledge points are directly explained. In the scheme, the length of the main teaching video is short because the explanation of the shallow knowledge points is omitted, and the content is concise. For students with good foundation, only the main teaching video can be watched and learned, and the learning efficiency is high. For students with poor foundation, when the teaching video plays to the shallow knowledge point (i.e. the first knowledge point), if the student has doubts about the shallow knowledge point and cannot understand the deeper knowledge points built on the basis of the shallow knowledge point, the student can learn the explanation video of the shallow knowledge point in the auxiliary teaching video, which can facilitate the learning of the subsequent deep knowledge points, and the student can learn more deeply.

This invention discloses a teaching video mind map generation system based on visual hierarchical analysis and multimodal fusion, comprising a visual feature extraction and analysis module, an audio analysis and node extraction module, and a multimodal fusion mind map generation module. The visual feature extraction and analysis module is used for video keyframe extraction, coarse-grained extraction of visual information, and fine-grained analysis of hierarchical relationships. The audio analysis and node extraction module is used for audio transcription and adaptive granular node extraction. The multimodal fusion mind map generation module is used to perform late-stage multimodal fusion of audio transcription information, mind map nodes, and hierarchical index tree information after visual analysis to generate teaching video mind maps. This invention significantly improves the logical accuracy and semantic expressiveness of automatically generated mind maps by fusing visual analysis and audio modal information for specific teaching video scenarios.

This application belongs to the field of educational informatization technology, specifically disclosing an intelligent evaluation method, device, equipment, and storage medium for classroom teacher-student interaction. Through this application, verbal interaction features of teachers and students in the classroom are determined based on recognition results; non-verbal interaction features are determined based on detection results; a quantitative matrix of classroom interaction is generated based on the verbal and non-verbal interaction features, and a classroom interaction feature tensor is constructed based on the quantitative matrix; intelligent evaluation of classroom teacher-student interaction is performed based on the classroom interaction feature tensor using a target tensor clustering model. By using the above method, the verbal and non-verbal interaction features of teachers and students in the classroom are determined based on the teaching video to be evaluated, and then intelligent evaluation is performed based on a target tensor clustering model that can reveal high-dimensional interaction relationships while maintaining the integrity of the data structure, thereby effectively improving the accuracy and fairness of evaluating teacher-student interaction.

The application provides a practical training teaching plan adaptation method and device, a terminal and a medium. The method comprises the following steps: obtaining student teaching videos, practical training simulation data and student question and answer data; obtaining a teaching resistance index according to the student teaching videos, the practical training simulation data and the student question and answer data; obtaining a key death weight according to the practical training simulation data and the student question and answer data; and adapting a practical training teaching plan according to the teaching resistance index and the key death weight. The method realizes reasonable allocation of the weight of key process details in the teaching plan, realizes an elastic adjustment mechanism of actual learning efficiency, and realizes self-evolution and precise adaptation of the practical training teaching plan by using a data-driven mode.

The application provides a teaching video quality evaluation method based on multi-modal semantic understanding and automatic construction of a knowledge graph, receives a teaching video resource to be evaluated and corresponding benchmark knowledge structure, and performs standardized processing on the teaching video resource; audio-video synchronization detection is performed on a local video file and an audio file; audio text information and visual text information in the teaching video resource are extracted; multi-modal knowledge extraction is performed on teaching content in the local video file; subgraph isomorphism mapping and topological comparison are performed on a video knowledge graph and the benchmark knowledge structure, and a teaching content completeness score is generated; and a comprehensive quality rating of the teaching video to be evaluated is generated, so that the application realizes automatic, refined and interpretable evaluation of teaching video quality in line with the laws of pedagogy.

A video interface system for learning from multiple tutorial videos, according to one embodiment of the present invention, comprises: a communication unit for acquiring a video of an educator, which is used for learning; and a processor for providing a video interface for playing the video, wherein the video interface includes: a video player area in which the video is played; and an information board area in which information on a currently playing video, a video in a played list, or a video in a list to be played is provided.