Bidirectional RNN for Overlapping Video Event Description
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Solution Overview
Problem
Existing video description generation methods struggle to accurately distinguish and describe overlapping events due to the use of unidirectional recurrent networks, leading to incomplete and non-distinctive descriptions.
Innovation Solution
The method employs a combination of forward and backward recurrent neural networks to extract and encode video features, enabling accurate positioning and differentiation of overlapping events by generating a forward hidden state sequence and a backward hidden state sequence, which are used to predict event intervals and generate descriptive words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a unidirectional recurrent network is used to generate video descriptions, then the model structure is simple and easy to implement, but overlapping events cannot be accurately positioned and distinguished
Solution Approach 1:
The patent applies bidirectional recurrent networks that process video frames in both forward and backward directions. The forward recurrent network captures temporal dependencies from past frames, while the backward recurrent network captures dependencies from future frames. This dual-directional approach enables accurate positioning of overlapping events by considering both historical and future contextual information, resolving the limitation of unidirectional networks that can only process sequences in one direction.
Solution Approach 2:
The patent introduces a temporal dimension by processing video frames at multiple time points (both past and future relative to the current frame). The bidirectional recurrent networks operate in two temporal dimensions simultaneously, allowing the system to distinguish overlapping events by examining their temporal relationships from both directions. This multi-dimensional temporal processing enables accurate event positioning that cannot be achieved with single-directional temporal processing alone.
2Productivity
If a unidirectional recurrent network is used for video description generation, then the computational process is straightforward, but events with close end times cannot be accurately positioned
Solution Approach 1:
The bidirectional recurrent network processes video sequences in reverse order as well as forward order. The backward recurrent network specifically processes frames from the end of the video backward to the current frame, allowing the system to accurately determine event end times by examining future frames. This reverse processing capability enables precise temporal boundary detection for events with close end times, overcoming the limitation of unidirectional processing that cannot access future temporal information.
3Measurement precision
If a bidirectional recurrent neural network is used to encode video features, then overlapping events can be accurately positioned and distinguished, but the model complexity increases
Solution Approach 1:
The patent merges the forward and backward recurrent networks into a unified bidirectional processing framework. Both networks share the same architectural components (recurrent units, weight matrices, bias terms) and process the same video input sequences, just in opposite temporal directions. This merging approach allows the system to achieve accurate event positioning through bidirectional processing while minimizing the increase in model complexity by reusing the same computational building blocks for both forward and backward processing.
4Manufacturing precision
If a bidirectional recurrent neural network is used to generate video descriptions, then event descriptions become more distinctive and accurate, but the computational cost increases
Solution Approach 1:
The patent applies bidirectional recurrent networks selectively to process only the portions of the video sequence necessary for event detection and description generation. Rather than processing the entire video sequence bidirectionally at full resolution, the system can focus computational resources on specific time windows or frames where events are detected. This partial application approach maintains high description accuracy for overlapping events while reducing overall computational cost by avoiding unnecessary processing of all video content.
Data Source
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AI summary
Disclosed in the present invention are a video description generation method and device, a video playing method and device, and a computer readable storage medium. The method comprises the following steps: extracting a video feature to obtain a video feature sequence corresponding to a video encoding moment in a video stream; encoding the video feature sequence through a forward recurrent neural network and a backward recurrent neural network to obtain a forward implicit state sequence and a backward implicit state sequence corresponding to each video encoding moment; and according to the forward implicit state sequence and the backward implicit state sequence, locating an event corresponding to each video encoding moment and an interval corresponding to the event at the video encoding moment, thereby predicting a video content description of the event. According to the method of the present invention, overlapping events are distinguished, and the interval corresponding to the event is introduced to predict a word corresponding to the generated event at the video encoding moment. For events that overlapped at the video encoding moment, even if overlap occurs, the overlap still corresponds to different intervals. The video content description of the event at the video encoding moment will have a very high degree of discrimination, and accordingly, there will be a higher degree of discrimination for a given video stream.