Temporal Insight Enhancement: Mitigating Temporal Hallucination in
Multimodal Large Language Models
- URL: http://arxiv.org/abs/2401.09861v1
- Date: Thu, 18 Jan 2024 10:18:48 GMT
- Title: Temporal Insight Enhancement: Mitigating Temporal Hallucination in
Multimodal Large Language Models
- Authors: Li Sun, Liuan Wang, Jun Sun, Takayuki Okatani
- Abstract summary: This study introduces an innovative method to address event-level hallucinations in MLLMs.
We propose a unique mechanism that decomposes on-demand event queries into iconic actions.
We employ models like CLIP and BLIP2 to predict specific timestamps for event occurrences.
- Score: 20.33971942003996
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have
significantly enhanced the comprehension of multimedia content, bringing
together diverse modalities such as text, images, and videos. However, a
critical challenge faced by these models, especially when processing video
inputs, is the occurrence of hallucinations - erroneous perceptions or
interpretations, particularly at the event level. This study introduces an
innovative method to address event-level hallucinations in MLLMs, focusing on
specific temporal understanding in video content. Our approach leverages a
novel framework that extracts and utilizes event-specific information from both
the event query and the provided video to refine MLLMs' response. We propose a
unique mechanism that decomposes on-demand event queries into iconic actions.
Subsequently, we employ models like CLIP and BLIP2 to predict specific
timestamps for event occurrences. Our evaluation, conducted using the
Charades-STA dataset, demonstrates a significant reduction in temporal
hallucinations and an improvement in the quality of event-related responses.
This research not only provides a new perspective in addressing a critical
limitation of MLLMs but also contributes a quantitatively measurable method for
evaluating MLLMs in the context of temporal-related questions.
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