Adaptive Sample Selection for Query-Relevant Data Processing
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Solution Overview
Problem
Existing neural network approaches for processing large numbers of data item samples, such as long video data, are either computationally expensive or limited by the number of samples they can process, often resulting in irrelevant or redundant data samples due to uniform sampling or fixed subset selection, leading to variable performance and inefficient resource usage.
Innovation Solution
Adaptive selection techniques using a selection neural network to generate relevance scores for both data item samples and placeholder samples, allowing for a dynamic subset selection based on semantic relevance to the query, thereby reducing unnecessary processing and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform sampling or fixed subset selection is used to process large numbers of data item samples, then the processing can be simplified, but the computational cost becomes extremely expensive or the number of processable samples is limited
Solution Approach 1:
The patent applies dynamics by replacing static fixed-size subset selection with a dynamic adaptive selection mechanism. The selection neural network dynamically determines the number and identity of samples to process based on the specific query and data item characteristics, allowing the system to adaptively adjust the processing scope rather than using a predetermined fixed size.
Solution Approach 2:
The patent changes the parameter of subset size from fixed to variable. Instead of processing a constant number of samples, the system varies the number of selected samples based on relevance scores generated by the selection neural network, enabling the system to handle different quantities of data item samples appropriately for each query.
2Ease of manufacture
If uniform downsampling is used to reduce data item samples, then the processing is straightforward, but redundant or irrelevant samples are included in the processed subset
Solution Approach 1:
The patent applies local quality by treating each data item sample differently based on its individual relevance score. Instead of uniform treatment, the selection neural network assigns different selection probabilities to different samples based on their semantic relevance to the query, ensuring that more relevant samples are prioritized for processing.
Solution Approach 2:
The patent substitutes the mechanical uniform sampling process with a neural-based semantic relevance assessment system. The selection neural network uses learned representations to evaluate and rank samples based on their semantic connection to the query, replacing simple random or fixed sampling with intelligent relevance-based selection.
3Device complexity
If a fixed number of data item samples is selected for processing, then the approach is simple, but the performance becomes variable depending on whether the fixed number is sufficient
Solution Approach 1:
The patent makes the selection mechanism dynamic by having the selection neural network determine the optimal number of samples to process based on the specific query and data item characteristics. This dynamic adjustment allows the system to adapt to varying requirements differentially between queries and data items, improving performance across diverse scenarios.
Solution Approach 2:
The system incorporates feedback through the selection neural network that continuously evaluates the relationship between queries and data item samples. The network uses the query context to generate relevance scores that feed back into the selection process, allowing the system to learn and adapt to different query-data item relationships over time.
Data Source
AI summary
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for receiving a query relating to a data item that includes multiple data item samples and processing the query and the data item to generate a response to the query. In particular, the described techniques include adaptively selecting a subset of the data item samples using a selection neural network conditioned on features of the data item samples and the query. Then processing the subset and query using a downstream task neural network to generate a response to the query. By adaptively selecting the subset of data item samples according to the query, the described techniques generate responses to queries that are more accurate and require less computation resources than would be the case using other techniques.