PulseAugur
EN
LIVE 17:14:16

Moment-GPT pipeline uses Llama 3 for zero-shot video moment retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Moment-GPT, a novel pipeline designed for zero-shot video moment retrieval that leverages existing multimodal large language models (MLLMs) without requiring fine-tuning. This approach aims to overcome limitations of current methods, such as reliance on expensive datasets and the issue of language bias in queries. Moment-GPT utilizes Llama 3 for query refinement, MiniGPT-v2 for adaptive span generation, and VideoChatGPT for final span selection, demonstrating superior performance on benchmark datasets like QVHighlights, ActivityNet Captions, and Charades-STA. AI

IMPACT This method could enable more efficient and accessible video analysis tools by reducing the need for specialized datasets and fine-tuning.

RANK_REASON The cluster contains an academic paper detailing a new method for video moment retrieval using existing LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Moment-GPT pipeline uses Llama 3 for zero-shot video moment retrieval

How we ranked this

Signal score
4 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
The cluster contains an academic paper detailing a new method for video moment retrieval using existing LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, model release
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

Full methodology in our editorial standards.

COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yifang Xu, Yunzhuo Sun, Benxiang Zhai, Ming Li, Wenxin Liang, Yang Li, Sidan Du ·

    Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large Language Models

    arXiv:2501.07972v2 Announce Type: replace-cross Abstract: The target of video moment retrieval (VMR) is predicting temporal spans within a video that semantically match a given linguistic query. Existing VMR methods based on multimodal large language models (MLLMs) overly rely on…

RELATED ENTITIES

RELATED TOPICS