Grounded Rationales for Fine-Grained Visual Reasoning
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Solution Overview
Problem
Conventional multi-modal large language models (LLMs) struggle with fine-grained visual inferences and spatio-temporal relationships in complex visual reasoning tasks, failing to effectively utilize heterogeneous sensory inputs for tasks like tracking objects under occlusion or answering queries that require detailed understanding of object interactions.
Innovation Solution
A neural network architecture that breaks down complex visual reasoning tasks into simpler steps, using spatial grid-level features and rationale-based training to map visual information to a textual domain, enhancing the capabilities of LLMs with lower-level visual capabilities through a 'look, remember, reason' framework, incorporating cross-attention layers and top-down attention mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multi-modal large language models are used for visual reasoning tasks, then they can process heterogeneous sensory inputs, but they fail to effectively perform fine-grained visual inferences and spatio-temporal relationships
Solution Approach 1:
The patent segments visual reasoning tasks into multiple discrete steps (e.g., object detection, relationship inference, temporal reasoning) that can be processed sequentially. Each step focuses on specific visual features, enabling fine-grained analysis while maintaining overall task versatility through the multi-step framework.
2Reliability
If neural networks use large amounts of training data to learn complex tasks, then learning accuracy improves, but training cost and time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training the model on surrogate tasks (simplified versions of the target task) before fine-tuning on the actual complex visual reasoning tasks. This staged approach allows the model to learn fundamental patterns efficiently, reducing the amount of data and time needed for final task mastery while maintaining high accuracy.
3Adaptability or versatility
If neural networks attempt to mimic human multi-step reasoning processes, then they can solve complex visual problems, but information density in visual domain makes it challenging to utilize extracted information
Solution Approach 1:
The patent introduces an intermediary reasoning framework that acts as a mediator between raw visual input and final conclusions. This framework includes intermediate representation layers that systematically organize extracted visual information, preserving critical details while transforming them into a format suitable for multi-step reasoning, thus preventing information loss.
4Measurement precision
If LLMs are enhanced with lower-level visual capabilities through rationale-based training, then visual reasoning performance improves, but computational resources and memory consumption increase
Solution Approach 1:
The patent applies partial action by implementing selective attention mechanisms that focus computational resources only on relevant visual features and reasoning steps needed for each specific task. Rather than processing all visual information uniformly, the model dynamically activates only the necessary visual capabilities and reasoning pathways, reducing overall computational consumption while maintaining high accuracy.
Data Source
AI summary
A processor-implemented method for generating grounded rationales for visual reasoning tasks includes receiving, by a first artificial neural network (ANN), an interleaved sequence of images and textual information. The first ANN extracts grid features of the images of the interleaved sequence of the images and the textual information to generate a representation of the interleaved sequence of the images and the textual information based on the grid features. A second ANN maps the grid features to a textual domain. The second ANN extracts visual information of the interleaved sequence of the images and the textual information based on the grid features in the textual domain. The second ANN determines a rationale based on the visual information. The visual information comprises one or more lower-level surrogate tasks.