Text-Conditioned Video Embeddings for Accurate Frame-Relevant Retrieval
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Solution Overview
Problem
Existing methods fail to effectively consider the semantic similarity between text and specific portions of videos, leading to inefficient video-text retrieval by aggregating frame representations without accounting for text relevance.
Innovation Solution
A text-conditioned video representation approach that uses text to affect the contribution of video frames, employing attention mechanisms to focus on relevant frames for a given text, generating a video embedding that accounts for text-specific relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame representations are aggregated across an entire video without text consideration, then the video representation is simple and computationally efficient, but the relevance score does not accurately reflect text-video semantic similarity
Solution Approach 1:
The video representation is made dynamic by conditioning it on the input text. Instead of a static aggregate representation, the system computes a text-conditioned representation that adapts to different text queries, allowing the same video to have different representations depending on the text being evaluated against.
Solution Approach 2:
The system shifts from uniform aggregation to localized frame selection. By using attention mechanisms, the system identifies and weights specific frames that are most relevant to the input text, rather than treating all frames equally. This local quality approach ensures that the representation focuses computational effort on the most semantically similar portions of the video.
2Reliability
If text-conditioned video representation is computed by considering text relevance for each frame, then the relevance score accurately reflects semantic similarity, but the computation time and complexity increase
Solution Approach 1:
The system replaces explicit mechanical frame-by-frame filtering with a learned attention mechanism. Instead of manually selecting relevant frames based on text content, the model uses neural network-based attention to automatically identify and weight important frames, substituting complex manual processing with optimized neural computations.
Solution Approach 2:
The system performs preliminary frame embedding computation and attention weight calculation in a structured manner, where frame representations are pre-computed and then weighted according to their relevance to the text. This preliminary organization of computational tasks optimizes the overall processing efficiency while maintaining high retrieval accuracy.
Data Source
AI summary
A text-video recommendation model determines relevance of a text to a video in a text-video pair (e.g., as a relevance score) with a text embedding and a text-conditioned video embedding. The text-conditioned video embedding is a representation of the video used for evaluating the relevance of the video to the text, where the representation itself is a function of the text it is evaluated for. As such, the input text may be used to weigh or attend to different frames of the video in determining the text-conditioned video embedding. The representation of the video may thus differ for different input texts for comparison. The text-conditioned video embedding may be determined in various ways, such as with a set of the most-similar frames to the input text (the top-k frames) or may be based on an attention function based on query, key, and value projections.