Video Temporal Segment Retrieval via Attention Encoder-Decoder
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Solution Overview
Problem
Existing video retrieval systems are unable to accurately retrieve specific moments within a video using natural language queries, as they are designed to retrieve whole videos rather than segments, and require labor-intensive paired video segment and description datasets.
Innovation Solution
A method and system that analyze video frame features to obtain temporal information, using an encoder and decoder with attention computation to determine video temporal segments corresponding to natural language retrieval descriptions, eliminating the need for paired datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing video retrieval systems use natural language queries to retrieve videos, then they can perform whole video retrieval, but they cannot retrieve specific temporal segments within videos
Solution Approach 1:
The patent divides the video into multiple temporal segments and processes each segment independently through the encoder-decoder architecture. The attention mechanism then selects and weights relevant segments based on the natural language query, enabling precise temporal localization without requiring the entire video to be processed as a single unit.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary component between the encoder and decoder. This attention mechanism computes relevance scores between query representations and temporal segments, acting as a mediator that selectively focuses computational resources on relevant video portions while filtering out irrelevant segments.
2Measurement precision
If previous moment retrieval systems are designed to retrieve temporal segments, then they can achieve segment-level retrieval, but they require labor-intensive paired datasets that are hard to acquire
Solution Approach 1:
The patent enables the system to automatically generate training data through self-supervised learning. The model learns to align natural language queries with temporal segments by processing video-content pairs without requiring manual annotation, allowing the system to train on large amounts of unannotated video data from sources like YouTube.
Solution Approach 2:
The patent performs preliminary processing of video data by extracting features and generating temporal segment representations before the actual retrieval task. This preliminary action prepares the data in a format that can be directly used for training the retrieval model without requiring subsequent manual labeling efforts.
3Productivity
If temporal action proposals are used for efficient action detection, then they can identify relevant temporal windows, but they cannot perform retrieval tasks
Solution Approach 1:
The patent designs a unified encoder-decoder architecture that serves multiple functions: it can perform temporal action detection through the attention mechanism while simultaneously enabling natural language-based retrieval. The same core components handle both detection and retrieval tasks, eliminating the need for separate specialized systems.
Solution Approach 2:
The patent merges the temporal action proposal mechanism with the retrieval system by integrating the attention computation into the encoder-decoder framework. This combination allows the system to leverage temporal segment proposals for efficient processing while adding retrieval capability through natural language query processing.
Data Source
AI summary
A method and a system for retrieving video temporal segments are provided. In the method, a video is analyzed to obtain frame feature information of the video; the frame feature information is input into an encoder to output first data relating to temporal information of the video; the first data and a retrieval description for retrieving video temporal segments of the video are input into a decoder to output second data; attention computation training is conducted according to the first data and the second data; video temporal segments of the video corresponding to the retrieval description are determined according to the attention computation training.