Multimodal Intent Discovery via Visual-Text Gating
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Solution Overview
Problem
Conventional systems for video intent discovery, particularly in longer videos like livestream tutorials, face inaccuracies due to off-topic conversations and noise, leading to inefficient manual identification and high costs, while existing methods struggle to distinguish between abstract and detailed intents effectively.
Innovation Solution
A multimodal intent discovery system that combines text and visual encodings using a gating component and cascaded cross-attention mechanism to filter out irrelevant information and accurately identify both abstract and detailed intents from video transcripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If extractive techniques are used to identify intents from spoken language in videos, then the system can process videos automatically, but accuracy deteriorates due to off-topic conversations and noise in longer videos
Solution Approach 1:
The system segments the video content into multiple modalities (visual frames, audio transcripts, captions) and processes each modality separately through dedicated encoders before integrating them. This segmentation allows the system to handle noise in one modality without it affecting the entire intent identification process, thereby maintaining accuracy while enabling automatic processing of long videos.
Solution Approach 2:
The system uses a composite multimodal representation that combines visual features from video frames, audio features from transcripts, and text features from captions. By integrating multiple modalities, the system creates a more robust representation that can distinguish relevant intent-related content from off-topic noise, improving accuracy while maintaining automated processing capability.
2Measurement precision
If manual intent identification is used, then accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables automatic self-service intent identification by training the multimodal model to identify intents without human intervention. The model processes video, audio, and text modalities automatically and generates intent predictions, eliminating the need for manual annotation while maintaining high accuracy through the integrated multimodal approach.
Solution Approach 2:
The system changes the processing parameters from manual human analysis to automated machine learning inference. By transforming the intent identification task into a computational problem that the trained model can solve automatically, the system maintains accuracy while dramatically reducing time consumption and cost.
3Device complexity
If text-only encoding is used for intent discovery, then the system complexity is reduced, but the ability to distinguish abstract and detailed intents deteriorates
Solution Approach 1:
The system merges text encoding with visual and audio encoding to create a unified multimodal intent discovery framework. By combining multiple encoding streams, the system achieves better intent distinction capability while managing complexity through a structured architecture that processes each modality independently before integration.
Solution Approach 2:
The system adds visual and audio dimensions to the traditional text-only approach. By incorporating video frames and audio transcripts as additional dimensions, the system enhances its ability to distinguish between abstract and detailed intents, providing richer contextual information for more accurate intent classification.
Data Source
AI summary
Systems and methods for intent discovery and video summarization are described. Embodiments of the present disclosure receive a video and a transcript of the video, encode the video to obtain a sequence of video encodings, encode the transcript to obtain a sequence of text encodings, apply a visual gate to the sequence of text encodings based on the sequence of video encodings to obtain gated text encodings, and generate an intent label for the transcript based on the gated text encodings.