Video Affective Reasoning With Event-Driven Frame and Tube Selection
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Solution Overview
Problem
Existing Multimodal Large Language Models (MLLMs) lack the ability to accurately predict and reason about viewer emotional reactions to videos due to limited awareness of emotional stimuli, leading to insufficient affective explanations and lack of interpretability.
Innovation Solution
A spatiotemporal stimuli-aware framework for video affective reasoning is introduced, incorporating frame-level awareness through event-driven frame sampling and token-level awareness via emotion-triggered tube selection, using a two-phase affective training protocol to enhance the reasoning capabilities of MLLMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Multimodal Large Language Models (MLLMs) are used for video understanding, then video content comprehension capability is improved, but emotional reaction prediction accuracy deteriorates
Solution Approach 1:
The patent segments the video into event-driven frames based on optical flow, separating emotionally significant moments from neutral content. This allows the model to focus computational resources on emotionally relevant segments, improving emotional prediction accuracy without sacrificing overall video comprehension capability
Solution Approach 2:
The patent applies local quality by identifying and emphasizing specific spatiotemporal regions (tubes) that trigger emotional responses. The emotion triggered tube selector identifies local regions with high emotional significance, allowing the model to assign different weights to different video regions based on their emotional impact
2Loss of information
If MLLMs focus on semantic content analysis, then content understanding is improved, but affective reasoning capability deteriorates
Solution Approach 1:
The patent merges semantic content understanding with affective reasoning by integrating the visual encoder, projector, and emotion triggered tube selector into a unified framework. The LoRA-adapted LLM combines semantic processing with emotional reasoning, allowing the model to maintain both content understanding and affective capability simultaneously
Solution Approach 2:
The patent introduces an intermediary projector layer that translates visual encoder outputs into a format suitable for the LLM. This projector acts as a mediator between visual processing and language generation, enabling the model to bridge semantic content and emotional reasoning without direct conflicts
3Productivity
If event-driven frame sampling is applied, then computational efficiency is improved, but frame coverage completeness may deteriorate
Solution Approach 1:
The patent applies partial action by sampling only the most emotionally significant frames rather than processing all frames uniformly. The optical flow-based event detection identifies key moments that drive emotional responses, allowing the model to achieve high computational efficiency while maintaining sufficient frame coverage for accurate emotional prediction
Data Source
AI summary
According to one aspect, spatiotemporal stimuli-aware video affective reasoning may include identifying one or more event-driven frames from a set of one or more frames of a training video based on an optical flow associated with one or more of the frames of the training video and training a projector based on the event-driven frames and an associated emotional response. The projector may receive an encoding of the event-driven frames and generate a visual token indicative of the event-driven frames based on the encoding of the event-driven frames.