Multimodal Entity Resolution Using Scene Understanding for Assistant Queries
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Solution Overview
Problem
Existing systems face challenges in resolving coreferences to entities across multiple input modalities, particularly in ambiguous user queries involving visual data, and in performing multimodal dialog state tracking and action prediction, which affect the accuracy and relevance of user interactions with assistant systems.
Innovation Solution
The assistant system employs a multimodal context integration of image, video, and text domains, utilizing a scene understanding engine and coreference resolution to disambiguate entities and track user intent, leveraging client-side and server-side processes for efficient and secure user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the assistant system integrates multiple input modalities (image, video, text) for comprehensive user interaction, then the system's understanding capability and response relevance are improved, but the complexity of coreference resolution and entity disambiguation increases significantly
Solution Approach 1:
The system segments the complex multimodal coreference resolution problem into separate processing streams for different modalities (visual data processing, text processing). Each modality is handled by specialized components that extract features independently, then the results are integrated. This segmentation reduces the overall complexity by avoiding the need to process all modalities simultaneously in a single complex system.
Solution Approach 2:
The patent introduces intermediate representations and processing layers that mediate between raw multimodal inputs and the final coreference resolution. Visual data is processed through scene understanding engines that generate intermediate semantic representations, which then interact with text processing results. These intermediaries simplify the integration process and make the overall system more manageable.
2Measurement precision
If the system performs detailed scene understanding and entity disambiguation to improve dialogue accuracy, then the quality of user interaction is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of visual data by continuously analyzing the camera feed and pre-processing images to identify and track objects before they become the focus of user queries. Scene graphs and object trackers maintain ready-state information about the environment, so when a user asks about an object, the system can quickly retrieve pre-analyzed data rather than processing from scratch.
Solution Approach 2:
The patent applies detailed scene understanding selectively rather than uniformly across all visual data. The system focuses computational resources on regions and objects that are relevant to the current dialogue context or user attention, rather than analyzing every pixel in every frame with equal detail. This localized approach maintains accuracy for relevant entities while reducing overall processing burden.
3Ease of operation
If the assistant system implements proactive dialog state tracking and action prediction, then the user engagement and naturalness of dialogue are improved, but the system complexity and difficulty of implementing accurate prediction increase
Solution Approach 1:
The system implements feedback loops where the dialog state tracker continuously monitors user responses and adjusts its predictions accordingly. Action predictions are made based on accumulated dialog state information, and the results feed back into updating the dialog state model. This feedback mechanism allows the system to learn from interactions and improve its predictions over time without requiring excessively complex initial architecture.
Solution Approach 2:
The dialog state tracking system is designed to be dynamic and adaptive rather than static. The system adjusts its tracking focus and prediction priorities based on the current dialogue context, user behavior patterns, and detected intent. This dynamic approach allows the system to maintain natural dialogue flow while managing complexity through adaptive resource allocation rather than fixed complex structures.
Data Source
AI summary
In one embodiment, a method includes receiving, at a client system, an audio input, where the audio input comprises a coreference to a target object, accessing visual data from one or more camera associated with the client system, where the visual data comprises images portraying one or more objects, resolving the coreference to the target object from among the one or more objects, resoling the target object to a specific entity, and providing, at the client system, a response to the audio input, where the response comprises information about the specific entity.